As deep space exploration missions continue to advance toward higher autonomy， longer operational duration， and more complex environments， the traditional task paradigm dominated by ground-based teleoperation can no longer satisfy the requirements for real-time， reliable， and efficient operations in lunar， Martian， and farther deep-space scenarios. Under conditions such as significant communication delay， limited prior environmental knowledge， strict onboard resource constraints， and high trial-and-error cost， future deep space platforms are expected to move beyond single-command execution toward integrated autonomous operation. In this context， embodied intelligence is gradually emerging as an important research direction for deep space exploration. The reason is that it provides a unified framework for coupling environmental perception， autonomous decision-making， and physical execution， which improves the adaptability and autonomy of exploration systems in long-duration extraterrestrial missions. This study systematically reviews recent progress in intelligent methods for deep space exploration from the perspective of embodied intelligence. Focusing on two central challenges—limited perception capability in highly unstructured extraterrestrial environments and insufficient autonomous decision-making capability under long-horizon， high-risk， and resource-constrained conditions—the study reviews recent advances in deep space visual perception and deep space embodied intelligence， and summarizes representative datasets， typical tasks， and evaluation protocols. For visual perception， the survey first reviews traditional image processing-based methods， including stereo terrain reconstruction， visual odometry， photogrammetric modeling， and large-scale localization. These methods established the technical foundation for early planetary exploration because they are grounded in explicit geometric constraints and therefore offer strong interpretability and engineering reliability. However， their effectiveness is increasingly challenged by weak textures， repeated patterns， severe illumination changes， occlusion， and long-range cumulative drift in lunar and Martian environments. The study then examines deep learning-based visual perception methods， which cover terrain semantic classification and segmentation， cross-domain adaptation， and multimodal terrain understanding. Compared with traditional geometry-driven approaches， deep models provide stronger semantic expressiveness and improved environmental understanding in complex planetary scenes. At the same time， their performance still depends heavily on data coverage， annotation quality， training strategy， and sim-to-real consistency， while onboard deployment is constrained by limited computation and energy budgets. From this perspective， the development of deep space perception is gradually shifting from closed-dataset recognition accuracy toward robust representation， long-term adaptability， and task-oriented environmental understanding. From the perspective of embodied intelligence， the study categorizes deep space tasks into three major classes： autonomous visual navigation， autonomous mission planning， and autonomous scientific operation. On this basis， the methodological evolution of deep space embodied intelligence can be viewed in three stages. The first stage is represented by teleoperation-centered control models， in which planning and key decisions are mainly completed on the ground， while the onboard system executes predefined instructions with limited rule-based local autonomy. Although this paradigm has been essential for ensuring mission safety and controllability， its strong dependence on human intervention limits its capability to support future long-duration missions in uncertain environments. The second stage is characterized by deep-learning-enhanced local autonomy. In this stage， learning models are embedded into specific modules of conventional engineering pipelines to improve terrain understanding， candidate path evaluation， risk estimation， target selection， and onboard data screening， while the overall decision and control chain remains largely interpretable and verifiable. The third stage involves multimodal large models and vision-language-action systems， which introduce higher-level cognitive capability into deep space systems. By unifying multimodal observations， state information， and task instructions in a shared semantic space， these methods enable cross-modal reasoning， task decomposition， dynamic replanning， and long-horizon decision organization. The survey argues that such models are promising for future high-autonomy missions. However， under current engineering conditions， they are more suitable as high-level cognition and task-orchestration modules than as direct substitutes for low-level planning and control. The study further reviews datasets and evaluation methods for visual perception and embodied intelligence. Compared with terrestrial benchmarks， publicly available deep space datasets remain limited in scale， annotation richness， and task diversity. For embodied intelligence， the challenge is even more fundamental， considering that datasets must support temporality， interaction， and closed-loop evaluation by including not only observations but also pose， action， state， and feedback information. As a result， high-fidelity simulation environments and digital twin-style benchmarks currently play an important role in supporting reproducible system evaluation. Correspondingly， evaluation protocols are also evolving from static recognition metrics toward task-level criteria involving safety， effectiveness， efficiency， robustness， and long-horizon reliability. Based on the abovementioned survey， the study discusses several future directions for deep space embodied intelligence， including perceptual generalization in strongly unstructured environments， trustworthy autonomous decision-making， system-level collaboration across perception and control modules， and lightweight deployment on resource-constrained platforms. In summary， deep space embodied intelligence is emerging as a system-level intelligence problem shaped by extreme extraterrestrial environments， long-duration mission chains， strict resource limitations， and high reliability requirements. Its future advancement depends not only on improving individual algorithms but also on establishing an integrated framework that unifies perception， decision-making， execution， validation， and deployment for real deep-space missions. This survey aims to provide a structured reference and clearer analytical perspective for the further development and application of autonomous intelligence technologies in deep space exploration.

Diffusion models have rapidly become the dominant paradigm of generative visual models. However， the memorization-forgetting mechanisms inherent in these models make them unintentionally memorize sensitive information from training datasets， which further aggravates the privacy and copyright issues in the image domain. Although the memorization and forgetting mechanisms have been deeply studied in the field of language models， a systematic review on diffusion models， which are the core technology of visual generation tasks， is still lacking. Our survey aims to bridge this gap， and the existing works are reviewed from five aspects critically. First， we briefly introduce the theoretical modeling and architecture of diffusion models， including the formulations of denoising diffusion probabilistic models， score-based generative models， and flow matching， as well as the architectures of U-Net， DiT， and MMDiT. Such theoretical and architectural foundations not only support the strong generative capability of diffusion models but also provide the necessary preliminaries for understanding why and how memorization arises during the iterative denoising process. On this basis， the definitions of memorization in diffusion models are introduced from non-temporal and temporal perspectives. For non-temporal diffusion models， the memorization can be divided into global memorization and local memorization， where the former focuses on the global similarity between generated and training images， and the latter is concerned with the partial replication of sensitive components such as faces， signatures， and watermarks. For temporal diffusion models， the memorization can be further decomposed into content memorization and motion memorization， given that the leakage in video diffusion models is reflected not only in the static appearance of objects but also in the dynamic motion patterns across frames. Once the manifestations of memorization are formally defined， a natural follow-up question is what factors cause such memorization behaviors and how they can be theoretically interpreted. Accordingly， the understanding of memorization in diffusion models is then discussed from the perspectives of model factor， data factor， and theoretical view. The model factor is focused on the influence of over-parameterization and architectural components such as the cross-attention modules， while the data factor reveals that the long-tailed distribution and duplicated samples can significantly increase the memorization risk. The theoretical view is supported by the manifold memorization hypothesis and the geometry-adaptive harmonic representation， which explain why diffusion models tend to memorize specific samples from a geometric standpoint. Building upon these qualitative understandings， more quantitative tools are required for the practical assessment of memorization risks. To this end， the quantification methods of memorization are introduced from the perspectives of a model auditor and a malicious attacker. The auditor-based methods can be divided into proxy- and replication-based methods. The proxy-based methods are designed to approximate the memorization score by influence functions， gradient variance， and geometric proxies such as the Hessian curvature. The replication-based methods utilize pixel-， perceptual-， and semantic-level similarity metrics， such as structural similarity index measure（SSIM）， learned perceptual image patch similarity（LPIPS）， self-supervised copy detection （SSCD）， and contrastive language-image pretraining（CLIP）， to detect the duplication between generated images and training images. The attacker-based methods can be divided into membership inference and extraction attacks， which are designed to determine whether a sample is in the training dataset or to recover the original training samples directly. The threat models can be further categorized into white-， gray-， and black-box settings according to the attacker’s knowledge of the target model. Given that these quantification methods have empirically verified the severity of memorization-induced privacy leakage， how to effectively mitigate such risks becomes the next critical concern. Therefore， the memorization mitigation methods under a unified framework of machine unlearning are introduced， including 1） differential privacy， 2） prompt optimization， and 3） machine unlearning. The differential privacy-based methods are typically applied in the pre-training stage and can provide formal privacy guarantees through input perturbation， output perturbation， or gradient perturbation such as DP-SGD， but at the cost of generative utility. The prompt optimization-based methods intervene at the inference stage by perturbing the text embedding or attention scores to prevent the model from generating memorized content， which is lightweight but less rigorous. The machine unlearning-based methods can be focused on fine-tuning the model parameters to redirect the memorized concepts to null concepts， locating and editing the memorization-related neurons， or applying closed-form parameter editing to remove the influence of specific samples or concepts in a post-hoc manner. The three families of methods are complementary to one another in terms of intervention stage， computational overhead， and privacy guarantee， and their integration provides a more comprehensive perspective on memorization mitigation in diffusion models. In summary， although considerable progress has been made along the abovementioned five aspects， several critical challenges still remain to be addressed in future research. 1） The privacy data processing pipeline and benchmarks of diffusion models are required to be standardized further， given that the absence of unified datasets and evaluation protocols hampers fair comparison and reproducibility. 2） The memorization definitions and machine unlearning algorithms that are more compatible with the characteristics of diffusion models are required to be developed， considering that the existing methods are mostly inherited from classifiers or language models and cannot fully exploit the iterative and cross-modal nature of diffusion processes. 3） The memorization-forgetting mechanisms in novel learning scenarios such as test-time training， federated learning， and continual learning are required to be explored， and the deployment in vertical domains such as medical imaging and financial generation is required to be accelerated for the cooperation of academia and industry. Furthermore， the data privacy policy-relevant ethical issues need to be considered for the responsible deployment of diffusion models in the future.

Portrait relighting is an important research direction bridging computer vision and computer graphics. Its core objective is to simulate the lighting effects under different real-world light sources by adjusting the lighting conditions of the person in an image to optimize the visual representation of images， improve the presentation of facial features， enhance the aesthetic effect and meet the needs of artistic creation. This technology has extensive application value， particularly in scenarios such as postprocessing of portrait photography， virtual reality， augmented reality， film special effects production， digital entertainment， human-computer interaction， intelligent monitoring and video conferencing. Relighting can not only restore or enhance the quality of an image but also add a new emotional atmosphere to it， making it more visually impactful and communicative. Therefore， how to relight human image efficiently， accurately， and naturally has been a key issue that researchers at home and abroad have focused on in recent years. The traditional methods for relighting portraits， such as the spherical harmonics illumination model， the Lambertian reflectance model， the Phong model， and the mixed model of ambient light and directional light， are mostly based on physical modeling ideas. These methods attempt to depict the interrelationships among lighting， materials， and geometry by establishing clear mathematical and physical models. However， real-world human faces are highly complex， with variations in skin color， uneven reflection attributes， and fine and irregular geometric structures， which cause traditional physical modeling-based methods encounter challenges such as large modeling errors， high computational complexity， and poor generalization ability when applied to real images. Especially in single-image input scenarios that lack multiperspective or depth information， traditional methods often fail to generate satisfactory relighting effects. With the rapid development of deep learning technology， the image relighting method based on neural networks has gradually emerged and quickly become the mainstream research paradigm. Compared with traditional methods， deep learning methods can automatically learn the complex nonlinear mapping relationship between illumination and facial features in images through large-scale data training， thereby achieving highly realistic and detail-rich relighting effects without explicitly modeling the physical process of illumination. This type of method usually has great flexibility， expressiveness， and scalability and can adapt to various complex lighting conditions and changes in face poses， showing enhanced robustness in practical applications.　To sort out the development context and research status of portrait relighting technology comprehensively， this study first starts from the research background， systematically summarizes the development process and key technological breakthroughs in this field， and clarifies its important value in various practical scenarios. This study also collates and compares the current mainstream portrait datasets and illumination datasets， covering synthetic data （such as face data generated on the basis of rendering engines） and real collected data （such as real illumination images obtained using multilight source shooting systems）. These datasets provide fundamental support for the training and evaluation of algorithm models. However， problems such as insufficient data scale， imperfect illumination labels， and uneven distribution of races and genders must be addressed urgently. The method review section classifies the current portrait relighting methods based on deep learning into three major categories： 1） the first type is methods combined with physical models， which attempts to establish a connection between the powerful representational ability of neural networks and the physical interpretability of traditional illumination models. For example， by introducing learnable spherical harmonic parameters， normal graphs， and reflection models， it guides the network to learn intermediate representations with physical relevance， making the generated results more realistic and controllable. 2） The second type is methods based on image transformation， which mainly relies on image-to-image mapping networks， such as U-Net structure， generative adversarial network， and self-attention mechanism. It directly learns the mapping relationship between the input image and the target illumination image and has advantages such as end-to-end training， high efficiency， and natural visual effects. However， certain deficiencies in terms of illumination control accuracy and geometric consistency still exist. 3） The third type is methods based on 3D reconstruction， which utilizes face reconstruction technology to first perform 3D reconstruction on the characters in the input image （such as restoring 3DMM parameters， depth maps， or normal maps）， then reconstruct the lighting conditions in 3D space， and finally project to generate a 2D image. This approach is usually superior to the pure image conversion method in terms of accuracy and control ability， but it has higher requirements for the quality of the input image and the accuracy of geometric reconstruction， and the computational cost is relatively large.　On the basis of a detailed analysis of the above three types of methods， this study also compares and evaluates the advantages and disadvantages of different methods from multiple dimensions such as the realism of the relighting effect， the degree of detail retention， computational complexity， and the generalization ability of the model. Meanwhile， in combination with the requirements of portrait relighting in practical applications， the applicability and robustness of various methods when dealing with complex factors such as different races， postures， the number of light sources and the position of light sources are explored.　Finally， considering the current development trends of deep learning and computer graphics， this study looks forward to the future research directions of portrait relighting algorithms. Future research may pay more attention to the lightweight design of the model， real-time processing capabilities， the fusion of multimodal inputs （such as combining semantic information， depth maps， etc.）， the improvement of cross-domain transfer capabilities， and more realistic and natural lighting style modeling. Meanwhile， the diversity of the dataset， the accuracy of the labels， and the rationality of the evaluation indicators also become important factors restricting the development of the technology. This study hopes to provide references and inspirations for subsequent research through a systematic review and analysis of portrait relighting technology and promote the further development of this field at the theoretical and application levels. In conclusion， this study aims to provide comprehensive technical references and theoretical support for relevant researchers through a systematic review and in-depth analysis of the existing research methods in the field of portrait relighting and to promote continuous progress in this field in terms of algorithm performance， application breadth， and visual quality.

With the rapid advancement of deepfake technologies， including generative adversarial networks （GANs）， autoencoders （AEs）， and diffusion models （DMs）， deepfake facial images have been increasingly exploited for malicious purposes， such as the creation of nonconsensual pornographic content， telecommunication fraud， and the propagation of political disinformation. In recent years， the continuous iteration of these core deepfake technologies has considerably lowered the technical threshold for generating high-fidelity fake facial images： GANs， with their adversarial training mechanism between generators and discriminators， enable the rapid synthesis of realistic facial features； AEs， relying on unsupervised learning and feature reconstruction capabilities， excel in simulating subtle facial expressions and skin textures； diffusion models， through step-by-step noise reduction and feature enhancement， have further broken through the limitations of traditional deepfake technologies in terms of image clarity and detail authenticity， making deepfake facial images increasingly difficult to distinguish with the naked eye and even challenging preliminary manual identification in forensic investigations. As an emerging form of digital evidence， deepfakes not only pervade contemporary criminal activities but also risk being weaponized for false accusations or the tampering of surveillance records， thereby severely undermining the reliability of facial image analysis in forensic science and threatening the integrity of the criminal justice system. To address the aforementioned challenges， this study systematically traces the developmental trajectory of key datasets and the technological evolution of deepfake facial images while conducting an in-depth dissection of the core architecture underlying their generation mechanisms， encompassing primary technical routes， theoretical underpinnings， and functional logics. In terms of dataset development， the evolution process from early small-scale， single-scene datasets （such as CelebA and LFW） to large-scale， multiscenario， high-diversity datasets （such as DFDC， FF++， and Celeb-DF） is sorted out， with the advantages and limitations of each type of dataset analyzed in terms of sample size， fake generation technology coverage， and scene diversity Moreover， the lack of standardized， forensic-oriented datasets has become one of the key bottlenecks restricting the practical application of deepfake detection technologies. In terms of technological evolution， the development of deepfake facial image technology is divided into three stages： the initial exploration stage dominated by GANs， the improvement stage supplemented by AEs， and the mature stage driven by diffusion models. The theoretical breakthroughs and technical improvements of each stage are elaborated， and the internal logic of the continuous improvement of fake image fidelity and the continuous reduction of generation costs is revealed. In the in-depth analysis of generation mechanisms， the core principles of the three mainstream technologies are focused on， the differences in their technical routes and functional characteristics are clarified， and the common key links and potential vulnerabilities in the generation process are summarized， laying a theoretical foundation for the subsequent design of targeted detection and identification methods. On the basis of 737 relevant articles retrieved and screened from the Web of Science database， a bibliometric analysis is performed via VOSviewer to identify research trends in the field and illuminate the substantial disconnect between existing detection technology research and the practical requirements for evidence authentication in judicial practice. The retrieval and screening process strictly followed the PRISMA guidelines： “deepfake”， “facial image”， “forensic science”， “identification”， and “detection” are adopted as key search terms； the retrieval time range is defined from the emergence of deepfake technology to the present； and irrelevant literature， review papers with low innovation， and duplicate publications are excluded through preliminary screening and full-text reading. The bibliometric analysis focuses on three core dimensions： research hotspots， institutional cooperation， and research frontiers. Results show that current research in the field is mainly concentrated on the optimization of detection algorithms （such as deep learning-based feature extraction and classification models）， while research on the translation of detection results into forensic authentication， the formulation of forensic identification standards， and the adaptation of detection technologies to judicial practice scenarios is relatively scarce. This disconnect is mainly reflected in two aspects： on the one hand， most existing detection technologies focus on technical accuracy， ignoring the admissibility requirements of judicial evidence （such as objectivity， authenticity， and relevance）； on the other hand， forensic examiners lack effective methods to integrate technical detection results into judicial authentication， leading to the situation where advanced detection technologies fail to be effectively applied in judicial practice. In response to the identified research-practice disconnect， this study proposes a classification of deepfake facial image detection methods into two complementary frameworks： technology principle oriented and application scenario adaptive， designed to address evidence authentication needs across distinct stages. The technology-principle-oriented framework takes the technical principles of deepfake generation as the core， classifies detection methods in accordance with the technical characteristics of different generation technologies （such as GAN-based detection， AE-based detection， and diffusion model-based detection）， and focuses on improving the accuracy and generalization ability of detection， thus being mainly applicable to the preliminary technical screening stage of evidence in forensic investigations. The application-scenario-adaptive framework takes the actual needs of different judicial stages （such as investigation， prosecution， and trial） as guidance， classifies detection methods in light of the scenario characteristics and evidence requirements of each stage， and focuses on enhancing the adaptability and operability of detection methods， thereby being mainly applicable to the formal evidence authentication stage in judicial practice. The two frameworks are complementary and mutually supportive： the technology-principle-oriented framework provides technical support for the application-scenario-adaptive framework， and the application-scenario-adaptive framework guides the direction of technical optimization， thus forming a complete detection system covering the entire process of forensic evidence authentication. In response to the evidentiary credibility challenges posed by deepfake facial images as a novel criminal vector， this study argues that forensic examiners should facilitate the translation of detection outcomes into forensic authentication， integrating detection results with the Bayesian inference framework to enhance their admissibility and probative force in judicial proceedings. Specifically， a Bayesian inference-based forensic authentication model is proposed. Technical detection results are taken as the prior probability， forensic examiners’ professional experience and case context are integrated to determine the likelihood ratio， and the posterior probability of the authenticity of facial images is ultimately calculated via the Bayesian formula to convert objective technical indicators into credible judicial authentication conclusions. Furthermore， forensic examiners should continuously improve their understanding of deepfake technologies， master the working principles and limitations of detection technologies， and avoid over-reliance on technical detection results to ensure the scientificity and rigor of forensic authentication. In conclusion， by bridging the advancements in computer vision technology with the practical demands of forensic evidence practice， this research articulates the logical pathway from technical detection to judicial authentication， providing a theoretically insightful and practically relevant reference for forensic science researchers and judicial practitioners alike. For forensic science researchers， this study clarifies the research direction of integrating technical research with judicial practice， identifies the key issues to be addressed in the future （such as the construction of forensic-oriented datasets， the optimization of detection algorithms adapted to judicial scenarios， and the formulation of forensic identification standards）， and offers a theoretical framework for subsequent research. For judicial practitioners， this study provides a practical operational path for the forensic authentication of deepfake facial images， facilitates the better grasp of the application methods and precautions of deepfake detection technologies， and elevates the efficiency and accuracy of digital evidence authentication. Additionally， this study offers a reference for the formulation of relevant laws and regulations， which helps improve the legal system of digital evidence authentication and promote the sound development of the criminal justice system in the era of deepfake technology.

AI-generated image detection technology aims to distinguish synthetic content from authentic imagery to safeguard media integrity and facilitate the subsequent forensic tasks further. In recent years， it has been a concern in the field of information security， especially in artificial intelligence-related industries such as judicial evidence verification， social media moderation， identity authentication， news broadcasting， and political security surveillance. Moreover， the growth of generative models has been promoting deep learning-based detection algorithms. In particular， the emergence of advanced techniques， such as the variational autoencoder， generative adversarial network， and denoising diffusion probabilistic model， has led to a qualitative leap in synthesis quality. However， a comprehensive review and analysis of state-of-the-art deep learning-based detection algorithms for different generative attacks are required to be realized to address the current theoretical fragmentation. Thus， we develop a systematic and critical review to explore the developments of AI-generated image detection in recent years. First， a comprehensive and systematic introduction of the detection field is presented from the following three aspects： 1） the evolution of forensic technology from handcrafted features to deep representations， 2） the prevailing deepfake datasets， and 3） the common evaluation metrics. Then， more extensive qualitative experiments， quantitative experiments， and generalization evaluations of representative detection methods are conducted on the public datasets to compare their performance. Finally， the summary and challenges in the forensic community are highlighted. In particular， some prospects are recommended further in the field of digital forensics. First， from the perspective of learning paradigms， the existing image detection methods can be divided into two fundamental categories， i.e.， the disproof paradigm aimed at identifying falsity， and the truth-preserving paradigm aimed at modeling reality. Specifically， the disproof paradigm focuses on extracting specific forgery traces including frequency domain artifacts and spatial inconsistencies， while the truth-preserving paradigm targets the modeling of intrinsic real image distributions to detect anomalies. In addition， the domain of deep learning-based detection algorithms can be classified into the convolutional neural network-based fusion framework and vision transformer-based framework from the aspect of network architectures. The disproof-based framework achieves the feature extraction of manipulation signatures by supervised binary classification and accomplishes deep feature discrimination via learning distinguishable boundaries between real and fake domains. To clarify the transition of methodologies， the truth-preserving framework is originated from one-class classification and reconstruction principles. From the perspective of the supervision paradigm， the deep learning detection methods can also be categorized into three classes， i.e.， supervised learning， unsupervised learning， and self-supervised learning. The supervised methods leverage labeled synthetic data to guide the training processes， and the unsupervised approaches construct loss functions via constraining the similarity between the input and the learned manifold of natural images. The self-supervised algorithms are associated with the masked image modeling framework in common. Our critical review is focused on the main concepts and discussions of the characteristics of each method for different forensic scenarios from the perspectives of the network architecture and learning paradigm. First， we summarize the limitations of different algorithms， such as the generalization failure of disproof methods and the modeling complexity of truth-preserving methods， and provide some recommendations for further research. Second， we briefly introduce the popular public datasets such as FaceForensics++ and Wilddeepfake and provide the interfaces related to download them for each specific detection scenario. Then， we present the common evaluation metrics in the forensic field from two aspects： classification-based evaluation metrics and robustness-based metrics designed for cross-generator settings. The generic metrics can be utilized to evaluate the precision and sensitivity of algorithms of those are accuracy-based， error rate-based， receiver operating characteristic-based， and area under the curve-based metrics in total. Some of the generic metrics， such as accuracy （ACC）， area under the curve （AUC）， equal error rate （EER）， and true positive rate （TPR）， are also used for the quantitative assessment of generalization. The specific metrics designed for localization consist of pixel-level intersection over union and bounding box precision that employ the pixel-wise masks as the reference ground truth. Third， we present the qualitative and quantitative results， and average inference times of representative alternatives for various forensic missions. Finally， this review critically analyzes the conclusion， highlights the challenges in the detection community， and conducts forecasting analysis， such as cross-domain image detection， high-level semantic consistency-driven detection， antiforensic robust detection， real-time mobile detection， explainable forensics based on physical imaging principles， detection under extreme compression， and comprehensive evaluation benchmarks. The methods， datasets， and evaluation metrics mentioned are linked at https://github.com/qqqqwddw/wesome-image-Detection and https://www.scidb.cn/detail?dataSetId=d6427185d9ff4c43bb6b190a117d723b.

With the continued evolution of artificial intelligence， 5G/6G communication， and multimodal interaction technologies， digital humans are rapidly transforming from static virtual avatars into intelligent agents capable of real-time， natural， and emotionally resonant communication. This transition marks a broader shift in human-computer interaction， moving beyond scripted responses toward context-aware， adaptive systems that can perceive， reason， and respond with human-like fluidity across multiple sensory channels. This paper presents a comprehensive review of real-time multimodal interactive digital humans， focusing on their conceptual evolution， technical framework， and key challenges. We trace the historical trajectory from early non-interactive virtual characters on platforms such as second life to today’s AI-driven agents that integrate language understanding， emotional intelligence， and physical embodiment in immersive environments. The survey systematically examines the core technologies underlying modeling， real-time driving， and rendering， and provides an integrated view of how multimodal data—including speech， facial expression， gaze， gestures， and physiological signals—can be perceived， fused， and generated to support coherent and lifelike human-machine interaction. From the perspective of modeling， we analyze the transition from traditional 3D parametric models to neural implicit representations such as NeRF and 3D Gaussian splatting， which enable high-fidelity reconstruction and photorealistic rendering while still supporting real-time performance. Recent advances in 3D Gaussian splatting， including reported rendering speeds of more than 300 frame/s on consumer hardware， have helped bridge the longstanding gap between visual fidelity and interactive responsiveness that previously constrained practical deployment. In terms of motion and expression control， the survey highlights the role of multimodal fusion and diffusion-based generation in synchronizing voice， facial animation， and body motion， thereby improving the naturalness and temporal consistency of interactive behavior. We critically examine the evolution from rule-based animation systems to end-to-end neural approaches that jointly model cross-modal dependencies， reducing the uncanny-valley effect through stronger temporal coherence and greater emotional authenticity. Rendering technologies are reviewed from both physically based and neural perspectives， with emphasis on balancing visual realism and latency in real-time deployment. Emerging hybrid approaches that combine explicit geometric representations with neural detail enhancement appear particularly promising for achieving both physical plausibility and computational efficiency in resource-constrained settings. Furthermore， the paper proposes a unified system architecture for digital humans consisting of four hierarchical layers： perception， fusion， generation， and extension， each responsible for multimodal sensing， semantic integration， expressive generation， and cross-domain adaptability， respectively. The perception layer integrates heterogeneous inputs ranging from microphone arrays and RGB-D cameras to emerging physiological sensors such as EEG and EDA， thereby establishing a rich basis for contextual understanding. Within this architecture， the integration of large multimodal models （LMMs） and large language models （LLMs） enables digital humans to perceive complex environmental and emotional cues， reason about user intent， and produce personalized， empathetic responses. Modern foundation models also support in-context adaptation， allowing digital humans to adjust their communication style dynamically according to interaction history， user characteristics， and situational context without explicit retraining. Representative technologies such as neural radiance fields， real-time diffusion-based speech synthesis， and emotion-driven dialogue generation are analyzed to illustrate their contributions to interactivity and immersion. Particular attention is given to latency-critical components， for which end-to-end response times below 200 ms are often essential to preserving conversational flow and user trust in real-world applications. Based on this synthesis， the paper identifies current challenges including latency optimization， multimodal alignment， semantic coherence， and emotional authenticity. We highlight the fundamental tension between computational complexity and real-time constraints， especially when deploying high-fidelity models on edge devices with limited power and thermal budgets. Addressing these issues will require advances in lightweight model compression， edge-native deployment， and cross-modal consistency learning. Promising directions include neural architecture search for modality-specific subnetworks and dynamic computation scaling based on interaction complexity. Looking ahead， digital-human research is expected to move toward more deeply integrated affective computing， multimodal reasoning， and socially adaptive intelligence， ultimately enabling human-machine interaction that is perceptually natural， emotionally grounded， and ethically trustworthy. At the same time， crucial issues such as digital identity， consent-based data usage， and robust watermarking mechanisms must be addressed to ensure responsible large-scale deployment. By summarizing state-of-the-art methods and mapping emerging trends， this study provides both theoretical insight and practical guidance for the development of the next generation of real-time multimodal interactive digital humans， with broad applications in education， healthcare， entertainment， and collaborative robotics.

ObjectiveThe acquisition of land surface change information is a fundamental pathway for understanding the evolution of the natural environment and the impacts of human activities. Land surface changes reflect the joint influence of natural processes and anthropogenic drivers on landscape pattern evolution. Such changes are not only governed by natural factors but are also closely intertwined with social and economic development. Timely and accurate monitoring and identification of these changes are crucial for gaining deeper insights into regional ecological succession and urban expansion processes， and they provide essential scientific support for territorial spatial planning， ecological and environmental protection， and social governance. Remote sensing change detection is an important research direction in the fields of remote sensing and geographic information science， and it has attracted increasing attention in recent years. The core objective of remote sensing change detection is to identify considerable changes in land-cover elements within the same geographical area across different time points by analyzing bitemporal or multitemporal remote sensing imagery. Most existing studies focus on bitemporal change detection. Although substantial progress has been achieved in terms of detection accuracy and computational efficiency， bitemporal approaches remain inherently limited in their ability to model temporal dynamics. In real-world scenarios， land surface changes often exhibit continuity， stage-wise progression， and multiscale temporal characteristics. Typical examples include the gradual expansion of urban areas， seasonal degradation and recovery of vegetation， and nonlinear postdisaster land surface restoration processes. Relying solely on two temporal snapshots makes it difficult to characterize such long-term evolutionary trends and complex dynamic patterns， and it also limits the ability to uncover latent trends associated with gradual or periodic changes. Moreover， given that only two temporal images are involved， bitemporal approaches are more susceptible to pseudo changes and misclassification when confronted with illumination variations， observation noise， and other acquisition-related inconsistencies. Consequently， exploring multitemporal change detection to better capture the dynamic evolution of land surfaces has become an important research direction. However， from a data perspective， high-resolution， high-quality multitemporal remote sensing change detection datasets that cover diverse land surface change types remain scarce. Most widely used change detection datasets remain limited to bitemporal imagery with corresponding annotations， primarily describing changes between two discrete time points. These datasets are often constrained in terms of the number of temporal phases， spatial coverage， image scale， and temporal span. As a result， they are insufficient for modeling and analyzing long-term， stage-wise land surface evolution processes， particularly in complex application scenarios such as urban expansion and ecological transformation.To address these limitations， this study constructs a high-resolution， multiregional， and multitemporal remote sensing change detection dataset， termed HR-MTCD （high-resolution multitemporal change detection）， with the aim of providing fundamental data support for dynamic land surface evolution modeling.MethodThe dataset covers five representative regions in China， including the Yangtze River Delta， the Pearl River Delta， the Beijing-Tianjin-Hebei region， the Chengdu-Chongqing region， and Northwest China. It integrates multiple types of land surface changes related to urban development and ecological evolution. The dataset spans a five-year period， with changes annotated at one-year intervals， and consists of 1 601 change groups and 8 005 remote sensing images with a spatial resolution of approximately 0.5 meters. The change categories primarily focus on prominent land surface changes， such as building construction and demolition， and large-scale vegetation changes while covering typical scenarios related to urban growth and ecological dynamics. This design ensures the dataset’s practical applicability and relevance， providing robust support for research on diverse land surface changes and for the development of multitemporal change detection algorithms.ResultSystematic experiments conducted using several mainstream change detection models reveal performance differences among models and validate the usability and the inherent challenges of the proposed high-resolution multitemporal dataset in real-world change detection tasks. Within a unified experimental framework， we further design progressive cross-temporal extension experiments. By comparing detection performance as training data from different temporal spans are incrementally incorporated， we evaluate the influence of multitemporal data on a model’s ability to recognize long-term， nonlinear， or gradual land surface changes. Experimental results demonstrate that the temporal dimension information contained in multitemporal remote sensing imagery plays a fundamental role in change detection tasks， offering new perspectives for models to understand dynamic land-cover evolution.ConclusionOverall， the construction of the HR-MTCD dataset provides an important data foundation for advancing remote sensing change detection from discrete bitemporal change identification toward comprehensive modeling of multitemporal land surface evolution processes.In future work， the dataset will be continuously expanded to cover a broader range of geographical regions and longer temporal spans to better capture diverse land surface dynamics under different environmental and socioeconomic conditions. In addition， we plan to incorporate multisource remote sensing modalities progressively， including optical imagery， synthetic aperture radar， and LiDAR data， thereby enabling complementary representations of land surface structure， texture， and physical properties. Beyond geometric and radiometric changes， semantic-level change information will also be introduced to describe higher-level land-cover transitions and functional transformations. The dataset is publicly available at https://doi.org/10.57760/sciencedb.j00240.00110.

ObjectiveMountain roads are vital infrastructure for transportation， logistics， and socioeconomic development in remote， topographically challenging regions. They provide essential connectivity for education， healthcare， resource transport， and disaster relief， yet their construction and maintenance are extremely difficult because of steep slopes， unstable geology， and frequent natural hazards. Despite their importance， automatically extracting mountain roads from high-resolution remote-sensing imagery remains a longstanding challenge. Complex terrain introduces severe occlusion and illumination variation， while the spectral appearance of roads is easily confused with bare rock， dry riverbeds， and agricultural paths. In addition， mountain roads are typically narrow， winding， and discontinuous， which differs substantially from the wide， regular patterns found in urban or rural road networks. Existing public road-extraction datasets， such as SpaceNet and DeepGlobe， were developed primarily for flat or urbanized environments. As a result， they fail to capture the spectral， geometric， and contextual complexity of mountain roads， leading to poor generalization when deep-learning models are transferred to high-relief regions. To bridge this gap， we construct AsiaMountain-Road， the first high-resolution， multi-country benchmark dataset dedicated to mountain-road extraction， and propose a novel multi-resolution adaptive feature dual aggregation network （MR-AFDANet） to evaluate and enhance model adaptability in diverse mountainous environments.MethodThe AsiaMountain-Road dataset was built from 29 high-resolution satellite-image areas selected from representative mountainous regions in 17 Asian countries， covering a total of 1 836 km² and providing detailed surface information for fine-scale mapping. All imagery underwent radiometric calibration， orthorectification， and mosaicking， followed by manual annotation by trained experts. Each region was labeled with fine-grained binary road masks to ensure pixel-level accuracy. To improve usability， auxiliary metadata， including elevation maps and land-cover references， were also provided. For algorithmic evaluation， we benchmark several mainstream deep-learning models—D-LinkNet， OareNet， C2Net， AFDANet， RCFSNet， and RS-Mamba—to assess their adaptability to mountainous terrain. Building on these findings， we propose MR-AFDANet， a model specifically designed for complex terrain mapping. MR-AFDANet uses a ResNet34 encoder as its backbone， consisting of four downsampling convolutional blocks. Each block is followed by a SEAF （spatial-enhanced adaptive feature fusion） module， which adaptively fuses intra-block and residual features while strengthening spatial information through multi-scale convolutions and attention mechanisms. In the decoder， an strip refinement attention decoder （SRADecoder） progressively fuses multi-resolution features to refine road boundaries， preserve continuity in narrow and winding segments， and suppress background noise caused by shadows and vegetation. This design enables the network to preserve thin road topology even under severe spectral interference.ResultExperimental evaluation on the AsiaMountain-Road benchmark demonstrates the substantial difficulty of road extraction in mountainous regions. Compared with urban or plain datasets， mountain imagery contains more complex topography， stronger illumination variation， and more severe occlusion from vegetation and terrain shadows， all of which significantly degrade segmentation accuracy. Across six state-of-the-art baselines， all models show varying degrees of road omission and fragmentation， particularly on narrow， winding， and shaded mountain roads. The results further show that models trained only on source-domain data struggle to generalize to the mountainous target domain because of large distributional discrepancies. Quantitative comparisons indicate that MR-AFDANet achieves the best overall performance， obtaining the highest intersection over union（IoU）， F1-score， and recall among all evaluated methods. These improvements confirm that combining multi-scale feature fusion with the SRADecoder enhances both global consistency and fine-grained boundary precision. Visual analysis further verifies this advantage： MR-AFDANet produces more continuous and complete road extractions under severe occlusion， maintains connectivity along steep and curved routes， and reduces false detections on spectrally similar surfaces.ConclusionWe present AsiaMountain-Road， the first high-resolution， multi-country dataset dedicated to road extraction in mountainous regions. Covering 29 representative areas across 17 Asian countries， it provides detailed road-mask annotations together with auxiliary contextual and topographic data， filling an important gap in current remote-sensing resources. Baseline experiments show that existing state-of-the-art methods struggle with narrow， winding， and occluded roads in complex terrain. To address these challenges， MR-AFDANet integrates SEAF modules， a dual-resolution feature-fusion mechanism， and the SRADecoder to recover thin road structures more precisely and improve robustness across highly variable mountain landscapes. Overall， AsiaMountain-Road narrows the gap between real-world geospatial challenges and current computer-vision capabilities by offering both a demanding benchmark for algorithmic innovation and a valuable resource for applied geospatial analysis. The dataset and code associated with this paper are available at https://cstr.cn/31253.11.sciencedb.j00240.00109 and https://github.com/wyl-ucas/AsianMountain-DataSet.

ObjectiveIn the field of multimedia forensics， identifying the source of voice evidence is crucial for judicial applications and information security. Voice recordings often serve as key evidence in legal investigations， yet their evidential value is limited by the capabilities of current analytical techniques. Most existing approaches focus on identifying the mobile phone model but lack the precision to differentiate between individual devices of the same model. This limitation considerably reduces the utility of voice evidence because it cannot be uniquely linked to a specific device. As a result， such evidence is often treated as supplementary in court proceedings rather than being considered direct and legally valid proof. Fine-grained device identification inability hinders digital forensics effectiveness and highlights the need for advanced methodologies.MethodTo address this challenge， this study proposes a novel voice source identification （VSI） model based on multiscale feature fusion and dynamic enhancement. The model is designed to capture and amplify the subtle hardware-specific fingerprints embedded in speech signals. These fingerprints arise from variations in microphone and circuit components， making them unique to each device. The proposed framework consists of several interconnected modules. First， a residual-attention collaborative network is employed to enhance feature representation. This network combines the strengths of residual learning and attention mechanisms， enabling the model to focus on device-related characteristics while suppressing irrelevant speech content and environmental noise. The residual connections facilitate training stability， while the attention mechanism dynamically weights important features， improving discriminability. Second， a dual-path feature extraction structure is implemented. The overall feature extraction module uses an expression-enhanced time delay neural network to capture broad， context-rich device characteristics. This module processes long-term temporal dependencies， which are essential for identifying model-level attributes. Simultaneously， a local feature extraction module based on a multilevel residual SE-Res2Net architecture is used to capture fine-grained， high-frequency details. This component excels at identifying minute differences between individual devices by leveraging hierarchical residual connections and channel-wise attention， enabling multiscale feature learning within a unified framework. Third， a feature enhancement module incorporating feature recalibration and dynamic global filtering is applied. This module refines the fused multiscale features by emphasizing task-relevant information and filtering out noise. Feature recalibration adjusts channel-wise feature responses adaptively， while dynamic global filtering performs content-aware smoothing and enhancement， further strengthening the device-specific representations. Finally， a fine-grained classification model is constructed to achieve cross-level device identification. This classifier is trained to distinguish devices at multiple hierarchical levels， from brand and model down to the individual unit， improving accuracy and generalization. The entire architecture is optimized end-to-end， ensuring seamless integration of all components.ResultTo evaluate the effectiveness of the proposed VSI model comprehensively， a large-scale voice dataset comprising 14 mobile phone brands and 121 different individual devices was constructed. This dataset is designed to cover a wide range of scenarios and conditions， thereby providing a rigorous testbed for assessing the model's performance. The VSI model was evaluated using three key metrics： equal error rate （EER）， accuracy （ACC）， and minimum detection cost function （minDCF）. Results demonstrate the superior performance of the VSI model compared with existing methods. Specifically， the VSI model achieved an EER of 7.50%， an ACC of 89.97%， and a minDCF of 0.38. These results are remarkably better than those obtained by four other state-of-the-art methods reported in the literature. Compared with these methods， the EER was reduced by 4.75%， 4.71%， 5.44% and 3.46%， respectively. The ACC was increased by 3.98%， 2.20%， 4.90%， and 2.52% respectively， and the minDCF was decreased by 0.04， 0.04， 0.30， and 0.03， respectively. These improvements highlight the effectiveness of the VSI model in accurately identifying individual devices from voice signals. Moreover， the VSI model demonstrated robustness under varying conditions of voice duration， sampling rate， and encoding format. This robustness is crucial for practical applications because voice signals in real-world scenarios often exhibit variations in these parameters. The ability of the VSI model to maintain high performance across different conditions indicates its potential for widespread adoption in forensic and security-related applications.ConclusionThe findings demonstrate that the proposed VSI model remarkably advances the field of voice source identification by enabling precise， individual-level device recognition. By effectively extracting and enhancing hardware-specific fingerprints from speech signals， the model provides a reliable means of linking voice recordings to specific mobile devices. This capability enhances the evidentiary value of voice data in judicial contexts， allowing it to serve as direct and valid evidence. Furthermore， the model’s robustness to real-world variations ensures its practical applicability in diverse scenarios. These contributions offer strong technical support for judicial forensics， intelligent terminal authentication， and broader domains of multimedia security. Future work will focus on extending the model to handle more complex audio manipulations and integrating it with complementary forensic techniques.

ObjectiveSalient object detection （SOD） seeks to emulate human visual attention by locating and segmenting the most conspicuous objects in visual scenes. While deep-learning-based 2D RGB and RGB-D SOD methods have advanced substantially， 360° omnidirectional （panoramic） images introduce unique challenges， most notably spherical-to-plane projection distortions （e.g.， equal rectangular projection（ERP） artifacts）， severe polar-region deformation， and boundary discontinuities across the left-right wraparound. At the same time， depth cues are known to improve geometric reasoning in conventional RGB-D SOD， but their effective exploitation for 360° SOD is underexplored due to modality-alignment difficulties， noise in estimated depth， and the lack of fusion architectures tailored to panoramic projection effects. The objective of this work is to investigate and demonstrate how carefully designed cross-modal modeling and projection-aware feature processing can harness depth information to substantially improve salient object detection on ERP panoramic images， while remaining robust to depth estimation noise and projection artifacts.MethodWe propose an asymmetric dual-branch U-Net architecture for RGB-D 360° SOD that explicitly accounts for ERP projection properties and for cross-modal alignment. The model contains three novel components： 1） a panoramic-aware receptive field module （PA-RFM） that enhances direction-sensitive context modeling via a longitude-latitude-global triplet attention scheme and a ring-padding/cropping strategy to respect horizontal periodicity； 2） an attention-guided fusion module （AFM） that performs adaptive， dynamic weighting of RGB and depth channels using combined channel and spatial attention plus a lightweight modality weight generator to suppress noisy depth responses while preserving boundary cues； and 3） a cross-modal guided collaborative decoder in which a fusion-centric decoding path is explicitly guided by an independent high-resolution RGB decoding path via attentional gating， yielding superior edge and texture recovery. The RGB encoder is a pretrained SAM2 Hiera backbone with lightweight adapters to limit finetuning cost； the depth encoder is a ResNet-34 modified for single-channel input. PA-RFM employs a four-branch multiscale dilated design with a panoramic context aggregation module （PCAM） at each branch to merge longitude attention （via circular padding + horizontal convolutions）， latitude attention （via latitude-weighted vertical convolutions that attenuate polar exaggeration）， and global semantic attention （via latitude-weighted global average pooling （GAP） and channel recalibration）. Training is performed in PyTorch on NVIDIA RTX 3090 hardware using AdamW （lr = 1 × 10^-3， weight decay = 5 × 10^-4）， cosine annealing to 1 × 10^-7， batch size 4， up to 50 epochs， input resolution 512 × 1 024， and a structured loss composed of weighted BCE + weighted IoU with multilayer deep supervision.ResultWe evaluate the proposed model on two public panoramic SOD benchmarks augmented with depth maps produced by a state-of-the-art 360° depth estimator： 360-SOD （500 ERP images； 400 train/100 test） and 360-SSOD （1 105 ERP images； 850 train/255 test）. Quantitatively， our method achieves MAE = 0.015 1， max-F = 0.838 8， mean-F = 0.815 0， max-E = 0.933 1， mean-E = 0.924 0， and Sm = 0.873 6 on 360-SOD （same scores on 360-SSOD）， outperforming 10 representative RGB-D 2D SOD methods and seven representative RGB 360° SOD methods in objective metrics and visual quality. Relative to the strongest competing baseline， our mean absolute error（MAE） is reduced by 13.7%， max-F increases by 4.64%， mean-F by 3.24%， and Sm by 1.96%， demonstrating substantial gains particularly in boundary accuracy and structural consistency. Extensive qualitative comparisons show our model is markedly better at detecting objects in severely distorted polar regions， maintaining left-right boundary continuity， and recovering fine edges in cluttered or low-contrast scenes. Ablation studies confirm the contribution of each module： PA-RFM yields the largest single-module improvement （vs. RFB and a no-PA-RFM baseline）， PCAM’s three attention branches （longitude， latitude， global） act synergistically， and a ring-padding width of k = 2 provides the best tradeoff between boundary continuity and redundancy. AFM outperforms simple fusion strategies （add， concat， multiply） and several state-of-the-art （SOTA） fusion blocks （MobileSal， HIDANet， CPNet variants）. The cross-modal guided decoder meaningfully improves edge/detail recovery over a variant that omits the RGB decoding path. We also test robustness to different depth estimators and find consistent improvements over RGB-only baselines； performance varies modestly with depth source， with Joint_360depth yielding the best overall results but not causing catastrophic degradation when replaced.ConclusionThis study demonstrates that depth information can be effectively and robustly exploited for 360° omnidirectional SOD when combined with projection-aware receptive field design and modality-aware fusion and decoding strategies. The proposed PA-RFM addresses ERP-specific distortions （horizontal periodicity and polar exaggeration） via circular padding， latitude weighting and global recalibration； AFM adaptively reconciles noisy depth with RGB structure； and the cross-modal guided collaborative decoder preserves high-resolution RGB priors to restore fine boundaries and textures lost in fused representations. Together， these innovations lead to consistent and remarkable gains across standard panoramic SOD benchmarks， especially in polar regions and at projection boundaries. The approach is general and can be extended to other panoramic vision tasks and to setups with sensor-captured depth； future work will explore domain adaptation to real depth sensors， multiprojection fusion （ERP + CMP）， and model compression for mobile/AR/VR deployment.