ObjectiveHyperspectral image （HSI） classification is an important task in remote sensing image interpretation because HSI data contain continuous spectral responses that are useful for fine-grained land-cover recognition. Existing deep learning methods， especially convolutional neural networks （CNNs）， have achieved high classification accuracy under the closed-set assumption. However， this assumption requires that all test classes have been observed during training， which is often inconsistent with real remote sensing applications. In large-scale and dynamic scenes， a trained model may encounter undefined or newly emerging land-cover categories. Conventional closed-set classifiers usually force such unknown samples into one of the known classes and may produce overconfident predictions. In addition， HSI data are affected by illumination variation， atmospheric scattering， sensor noise， and mixed pixels， resulting in the phenomena of same material with different spectra and different materials with similar spectra. These factors make hyperspectral open-set recognition （OSR） more challenging than ordinary closed-set classification. To address these problems， this paper proposes a frequency knowledge-guided contrastive learning method， called FKCL， for hyperspectral open-set recognition. The main purpose is to improve the rejection ability for unknown land-cover classes while maintaining reliable classification performance for known classes.MethodThe proposed FKCL framework integrates data-driven spatial-spectral representation learning with frequency-domain physical prior knowledge. The whole network consists of two collaborative branches. The first branch is a spatial-spectral feature extraction branch based on a 3D-2D hybrid convolutional neural network. It uses 3D convolution to capture local spectral correlations and then uses 2D convolution to extract spatial contextual information from hyperspectral image patches. The extracted high-level representation is projected into a compact feature space， where class-wise metric learning is performed. The second branch is a frequency-domain physical attribute extraction branch. Instead of using the average spectrum of the whole patch， this branch uses the central pixel spectrum because the category label of a hyperspectral patch usually corresponds to the central pixel. The central spectral sequence is transformed into the frequency domain by real fast Fourier transform （RFFT）， and its magnitude spectrum is used to describe the frequency fluctuation pattern of the ground object. This design preserves the correspondence between the original spectral reflectance curve and its physical frequency representation， and avoids the interference caused by heterogeneous neighboring pixels. To obtain stable class-level frequency representations， an exponential moving average （EMA） mechanism is introduced to update the frequency prototype of each known class during training. The EMA update reduces prototype fluctuation caused by small-batch sampling and gradually accumulates the stable frequency pattern of each known category. For feature-space discrimination， the class anchor clustering （CAC） loss is adopted. The anchor loss pulls samples toward their corresponding class anchors， whereas the tuplet loss pushes samples away from non-corresponding anchors， thereby improving intra-class compactness and inter-class separability. During inference， FKCL uses a dual-distance decision strategy. The semantic feature distance and the frequency-domain prototype distance are fused by a frequency weight selected on the validation set， and the open-set rejection threshold is also determined on the validation set. The test set is used only for final evaluation， so no test information is involved in threshold selection.ResultExperiments were conducted on three public HSI benchmark datasets， namely WHU-Hi-LongKou， Pavia University， and Salinas. A class-holdout protocol was adopted to construct pseudo-unknown classes. In this protocol， selected categories are completely removed from training and appear only during validation and testing for unknown rejection evaluation. For WHU-Hi-LongKou， the Mixed weed class was held out as the pseudo-unknown class. For Pavia University， the Shadows class was held out. For Salinas， Stubble， Lettuce_romaine_6wk， Lettuce_romaine_7wk， Vinyard_untrained， and Vinyard_vertical_trellis were treated as pseudo-unknown classes. The proposed FKCL method was compared with six representative closed-set and open-set methods， including DBDA， SSFTT， CACL， SSMTL， HyperCASR， and SS-MTr. The evaluation metrics included overall accuracy （OA）， average accuracy （AA）， Kappa coefficient， area under the receiver operating characteristic curve （AUROC）， and unknown class accuracy. Experimental results show that FKCL achieves a better balance between known-class classification and unknown-class rejection. On WHU-Hi-LongKou， FKCL achieved an OA of 92.38%， an AA of 92.58%， and a Kappa coefficient of 90.08%， while its unknown class accuracy reached 25.20%， which was higher than most deep closed-set comparison models. On Pavia University， FKCL achieved an OA of 92.02%， an AA of 89.13%， a Kappa coefficient of 89.48%， an AUROC of 99.96%， and an unknown class accuracy of 100.00%， indicating that the frequency-guided dual-distance strategy can effectively identify the held-out Shadows class. On Salinas， the proposed method maintained high classification performance for known classes， but the rejection of pseudo-unknown samples remained more difficult because several unknown classes had very high spectral similarity to known classes. This result indicates that the open-set difficulty is closely related to the physical and spectral separability between known and unknown categories. The ablation analysis further shows that the frequency-domain branch， frequency prototype learning， and dual-distance decision mechanism contribute to the improvement of open-set detection performance. In particular， removing the frequency prototype EMA weakens unknown rejection， demonstrating that stable frequency prototypes are important for modeling class-level physical attributes.ConclusionThe proposed FKCL method introduces frequency-domain knowledge into hyperspectral open-set recognition and constructs a collaborative framework that combines deep spatial-spectral features with physical spectral priors. Compared with single-branch data-driven models， FKCL uses RFFT-based frequency representations and EMA-updated frequency prototypes to provide complementary information for distinguishing spectrally similar but physically different land-cover categories. The CAC-based metric learning module constructs a compact and separable feature space for known classes， and the dual-distance decision strategy further improves the ability to reject samples outside the known-class distribution. Under the class-holdout pseudo-unknown evaluation protocol， FKCL reduces the overconfidence problem of conventional deep models and improves the discrimination of unseen categories. Nevertheless， when unknown classes are extremely similar to known classes in both the original spectral domain and the frequency domain， open-set rejection remains challenging. Future work will focus on adaptive frequency-weight learning， more realistic open-world remote sensing scenarios， cross-region generalization， dynamic newly emerging categories， and open-set recognition with multimodal remote sensing data.

ObjectiveVisual imitation learning aims to learn control policies directly from high-dimensional image observations. It is important for robotics tasks， where agents often need to make decisions from visual inputs. However， compared with imitation learning based on low-dimensional proprioceptive states， visual imitation learning still suffers from a clear performance gap. One major reason is that behavior-related differences in pixel observations are often subtle. As a result， the visual encoder may fail to learn sufficiently discriminative state representations. This problem becomes more serious in visual adversarial imitation learning. In this framework， the discriminator needs to distinguish expert samples from agent samples and provide reward signals for policy learning. If the learned visual representation is weak， the discriminator may produce unstable or inaccurate rewards. This further affects policy optimization. Existing visual adversarial imitation learning methods mainly focus on the distinction between expert samples and agent samples. They usually treat this distinction as a static discrimination problem. However， they do not fully exploit the internal structure of agent samples stored in the replay buffer. They also do not explicitly model the dynamic evolution of agent samples during training. In practice， the quality of agent samples gradually improves as the learned policy approaches the expert policy. Ignoring this process may limit the quality of visual representation learning. To address these issues， this paper proposes a visual adversarial imitation learning method based on calibrated contrastive learning.MethodThis paper introduces a calibrated contrastive representation learning method in the visual adversarial imitation learning framework. The main idea is to improve the discriminative ability of the visual encoder by contrastive learning. Specifically， similar states are pulled closer in the feature space， while different states are pushed apart. In this way， the encoder can learn more effective visual representations for adversarial imitation learning. Different from existing methods that mainly focus on the static distinction between expert samples and agent samples， the proposed method further explores the internal sample structure in the replay buffer. It also models the dynamic improvement of agent sample quality during training. In the proposed framework， agent samples are regarded as a mixture of high-quality samples and low-quality samples. High-quality agent samples may be close to expert samples， especially in the later stage of training. Low-quality agent samples are still far from expert behavior and should not be treated as expert-like samples. Based on this observation， this paper introduces a calibrated supervised contrastive loss. This loss adaptively adjusts the contrastive relationship between agent samples and expert samples. It reduces the risk of incorrectly pushing high-quality agent samples away from expert samples. At the same time， it preserves the discrimination between expert behavior and low-quality agent behavior. The proposed method therefore improves both visual representation quality and adversarial training stability. The final objective combines the adversarial imitation learning loss with the proposed contrastive representation learning objective. The whole framework can be optimized in an end-to-end manner.ResultExperiments are conducted on 9 continuous control tasks from the DMControl Suite. These tasks cover different types of control behaviors， including balancing， locomotion， and complex body movement. The experimental results show that the proposed method achieves higher cumulative rewards on multiple tasks. It also shows better sample efficiency in the early training stage. Compared with the representative method PCIL， CAIL improves the average performance by 22.6% at 1M training steps. This result indicates that calibrated contrastive representation learning can effectively improve visual adversarial imitation learning. The ablation study further verifies the effectiveness of the proposed contrastive losses. The unsupervised contrastive loss on agent samples improves the use of the replay buffer. It helps the encoder learn from a large number of dynamically collected visual states. The calibrated supervised contrastive loss further improves performance by modeling the changing quality of agent samples. The results show that these components are complementary. The full CAIL model achieves better performance than the version without calibration. This demonstrates the importance of dynamically modeling agent samples during training. In addition， visualization results show that the discriminator learned by CAIL can focus more accurately on behavior-related regions， such as the joints and body parts of the agent. These regions are important for distinguishing different motion states. The visualization results further support that CAIL learns more meaningful and discriminative visual representations.ConclusionThis paper proposes a calibrated contrastive visual adversarial imitation learning method for high-dimensional visual control tasks. The proposed method makes better use of agent samples in the replay buffer. It also explicitly models the change of agent sample quality during training. By introducing calibrated contrastive representation learning， the method improves the discriminative ability of visual state representations and stabilizes adversarial training. Experimental results on DMControl tasks show that the proposed method achieves better average performance and sample efficiency than representative visual imitation learning methods. The proposed framework provides an effective representation learning strategy for imitation learning from high-dimensional image observations.

ObjectiveWith the rapid proliferation of unmanned aerial vehicles （UAVs） in civil and commercial fields， the demand for robust low-altitude security， airspace management， and intelligent visual surveillance systems has grown exponentially. Precise and real-time visual detection of UAV targets in continuous video streams is the core link of these systems. However， in practical uncontrolled surveillance scenarios， UAVs are usually located at extreme distances from the camera， occupying only a tiny fraction of pixels on the imaging plane， thus lacking clear morphological outlines， observable textures， and structural details. They are highly susceptible to being obscured by complex and dynamic backgrounds such as moving clouds， fluctuating lighting， and cluttered ground landscapes. Traditional single-frame object detection algorithms segment continuous video streams into isolated static images for frame-by-frame processing， fundamentally ignoring the crucial temporal motion information between frames. This leads to severe representation limitations when facing extremely small targets， with persistently high missed detection and false alarm rates. Existing temporal detection methods based on 3D convolution or Transformer can capture temporal features， but their huge parameter amount and computational overhead make them impractical for deployment on resource-constrained real-time security systems. This paper aims to break through the performance bottleneck of pure spatial detection frameworks and achieve high-precision and real-time detection of tiny UAV targets in complex backgrounds.MethodThis paper proposes a Temporal Motion-Aware Dual-Branch Network （TMAD-Net）. A dual-branch decoupling design is adopted at the data input end to physically separate spatiotemporal multimodal information. Specifically， the spatial semantic extraction branch processes multi-frame stacked raw RGB images to extract rich spatial appearance， local texture， and global contextual semantic features of the target and its surrounding environment. The parallel explicit motion prior branch is dedicated to processing inter-frame difference images between adjacent frames that have undergone absolute value operation and morphological denoising filtering. This branch filters out static background interference， camera jitter， and sensor noise， and generates a high-purity motion saliency map that highlights the high-frequency temporal motion trajectory of the flying UAV. The two data streams independently pass through the shallow network to complete high-dimensional feature extraction， and are then cascaded and concatenated along the channel dimension to maximally preserve multimodal spatiotemporal information. Subsequently， a motion-spatial adaptive fusion module is introduced to perform adaptive weight calibration on the fused features from both motion and spatial dimensions， so as to enhance the feature response of weak target signals and suppress irrelevant background noise. Finally， 1×1 convolution is applied to accomplish cross-channel dimensionality reduction and feature mapping， and the calibrated spatiotemporal features are seamlessly fed into the subsequent backbone network to complete target detection.ResultExtensive comparative tests and ablation experiments were conducted on the public ARD-MAV dataset and two private UAV datasets （Phone and DJI） collected in real-world scenarios， which are characterized by small target scales， varying illumination conditions， and low signal-to-noise ratios. Quantitative experimental results show that， compared with the standard baseline model， the proposed method achieves steady performance improvement on the ARD-MAV dataset： the mean average precision at 50% intersection over union （mAP50） increases from 0.264 to 0.588， and the mean average precision over the intersection over union range of 50% to 95% （mAP50-95） increases from 0.155 to 0.334；on the Phone dataset： the mAP50 increases from 0.802 to 0.887， and the mAP50-95 increases from 0.590 to 0.638.On the highly challenging DJI dataset， the proposed model achieves a remarkable performance improvement： the mAP50 surges from 0.316 of the baseline to 0.822， with a performance gain of over 160%， and the mAP50-95 jumps from 0.083 to 0.266. Compared with mainstream lightweight detection algorithms including YOLOv9s， YOLOv11s， and YOLOv12s， the proposed model achieves optimal detection accuracy on all three datasets. Even compared with the larger-scale RT-DETR-L model based on the Transformer architecture， the proposed model still achieves better overall performance in both detection accuracy and inference speed. Meanwhile， the proposed model has a parameter count of 11.33M， which is basically consistent with the 11.13M of the baseline model. It reaches an inference speed of 122 frames per second （FPS）， maintaining excellent real-time inference capability while realizing a steady improvement in accuracy. Ablation experiments further verify the effectiveness of the dual-branch architecture， spatial semantic extraction branch， and motion-spatial adaptive fusion module respectively， proving the rigor of the design logic of the proposed architecture.ConclusionThe TMAD-Net proposed in this paper successfully compensates for the inherent limitation of insufficient feature extraction capability of traditional single-frame detection algorithms when facing tiny targets. Through explicit separation and in-depth fusion of multi-frame RGB spatial appearance features and computationally optimized motion features， combined with the precise calibration capability of the motion-spatial adaptive fusion module， this method effectively isolates real targets from heavily cluttered and dynamic backgrounds， and effectively alleviates the long-standing performance bottleneck of video small target detection in complex real-world environments. Empirical results confirm that the proposed model significantly improves the detection accuracy and algorithmic robustness of UAV target recognition under non-ideal conditions， and provides a feasible， effective and novel visual detection paradigm for real-time video surveillance， public safety monitoring， and advanced low-altitude defense systems.

ObjectiveDriven by rapid advances in deep learning， text-to-speech （TTS） and voice conversion （VC） technologies have achieved unprecedented improvements in audio naturalness， fidelity and diversity， enabling large-scale and high-quality audio forgery. However， the widespread abuse of such deepfake technologies has posed severe and persistent threats to personal privacy， social credibility and even national security， triggering urgent demands for effective audio deepfake countermeasures. Existing research in this field mostly focuses on binary deepfake detection， which only judges whether an audio clip is forged but fails to identify the specific generation model or method behind the forgery. In contrast， audio deepfake attribution （ADA） targets tracing the exact source of forged audio by extracting unique model-specific artifacts， offering interpretable evidence for AI content governance， supporting complete evidence chains for judicial forensics， and promoting standardized supervision of the AI speech industry， thus emerging as a pivotal research hotspot in artificial intelligence security. Nevertheless， current ADA approaches are plagued by prominent defects that hinder practical deployment： on one hand， most methods rely on single-layer or shallow handcrafted features， lacking sufficient feature extraction capability to mine deep semantic information and fine-grained differences between diverse forgery models； on the other hand， they exhibit extremely limited generalization ability， especially in low-resource few-shot scenarios with scarce annotated training samples and open-set scenarios involving emerging unknown forgery models， suffering from drastic performance degradation and failing to adapt to the continuous iteration of speech synthesis technologies. Therefore， this study aims to develop a high-performance audio deepfake attribution method suitable for low-resource conditions， with strong discriminative ability for known forgery methods and reliable generalization for unknown forgery scenarios.MethodTo bridge the aforementioned research gaps， this paper proposes a novel Low-resource Audio Deepfake Attribution （LADAR） method dedicated to few-shot learning scenarios， which integrates innovative multi-layer feature fusion and multi-prototype learning mechanisms to break through the bottlenecks of existing methods. The core technical design is refined based on in-depth analysis of current mainstream attribution frameworks and their limitations. First， a multi-layer adaptive feature fusion module is built to leverage the powerful pre-training representation capability of Wav2Vec2-BERT 2.0， a state-of-the-art self-supervised audio model. Instead of using single-layer hidden states as in traditional methods， this module assigns learnable attention weights to aggregate multi-layer hidden features， and introduces a shallow bias factor to enhance the sensitivity to subtle low-level forgery traces that are easily ignored by deep layers； meanwhile， a learnable gating mechanism is adopted to dynamically fuse attention-weighted pooling and average pooling， generating compact and highly discriminative model embedding representations with optimized intra-class compactness and inter-class separability. Second， a K-means clustering driven multi-prototype learning module is designed to address the limitation of traditional single-prototype representation， which cannot cover the complex intra-class feature distribution of each forgery method. By generating multiple representative prototype vectors for each category， this module fully captures the feature variation patterns of the same synthesis model under different audio lengths， speakers and content conditions， strengthening the model’s robustness to sample variability. Finally， the attribution performance of LADAR is systematically evaluated under two critical experimental setups： closed-set scenario with known forgery methods and open-set scenario with unknown forgery methods， to fully verify its effectiveness and generalization.ResultsComprehensive comparative experiments are conducted against several representative state-of-the-art audio deepfake attribution methods， and the quantitative results fully validate the superiority of the proposed LADAR method. In the closed-set scenario targeting known forgery models， the LADAR method achieves significant performance gains， with overall accuracy improved by 35.25%， 26.26%， 9.22% and 5.65% respectively compared with baseline methods， and the F1-score， a comprehensive metric balancing precision and recall， increased by 38.82%， 27.42%， 7.23% and 5.74% correspondingly， reflecting its outstanding ability to accurately distinguish different known forgery methods. In the more challenging open-set scenario involving unknown forgery models， which is highly consistent with real-world application environments， LADAR still maintains stable and efficient performance： its accuracy is enhanced by 15.80%， 37.51%， 10.73% and 10.42% respectively， and the F1-score is elevated by 20.29%， 34.17%， 11.40% and 12.33% respectively compared with contrast methods. The experimental results demonstrate that LADAR effectively alleviates performance deterioration caused by insufficient training samples and inconsistent feature distribution of unknown samples， solving the core pain points of weak generalization and poor fine-grained recognition in existing methods.ConclusionThe systematic experimental analysis confirms that the proposed LADAR method effectively addresses the key technical challenges in audio deepfake attribution， with remarkable advantages in accuracy， robustness and cross-scenario generalization. By optimizing the feature extraction pipeline and innovating the prototype learning mechanism， it overcomes the inherent defects of insufficient feature mining and limited adaptability to unknown samples in traditional attribution methods. This method provides reliable， interpretable and efficient technical support for practical high-demand scenarios including judicial forensics， deepfake content source tracing， network audio supervision and national security defense， and also offers a feasible technical reference for other low-resource audio security tasks. In the context of continuous evolution of deepfake technologies， the LADAR framework can extend to new speech synthesis models， enabling sustainable AI audio security governance.

ObjectiveMultispectral remote sensing forest image change detection aims to identify temporal variations in land-cover types or surface conditions by comparing multispectral images acquired over the same area at different times. This technique has been widely applied in ecological monitoring and natural resource management， among which forest change detection is of particular importance for tracking forest cover dynamics， assessing ecosystem function changes， and supporting forest resource conservation. Multispectral remote sensing images， acquired through observations across multiple discrete spectral bands， can effectively characterize spectral response differences associated with changes in land-cover types and surface conditions. Meanwhile， they provide large-area coverage and periodic observations with controllable data acquisition costs， thereby offering stable and discriminative data support for long-term and continuous forest change monitoring. Consequently， multispectral remote sensing image–based change detection has become an essential technical approach for forest resource monitoring. In early studies， change detection was mainly conducted using traditional image processing and pattern recognition techniques， where changes were modeled based on manually designed features and predefined thresholds. Although these methods are relatively simple and interpretable， they are highly sensitive to noise， illumination variations， and imaging condition discrepancies， which limits their reliability in complex scenarios. With the rapid advancement of deep learning， convolutional neural network （CNN）–based methods have become the dominant paradigm for remote sensing change detection due to their strong capability in automatic feature representation and end-to-end learning. Siamese architectures further improve detection performance by explicitly modeling feature differences between bi-temporal images. However， the fixed receptive fields of CNNs restrict their ability to capture long-range dependencies and global contextual information. To alleviate this limitation， attention mechanisms have been introduced to emphasize change-relevant features across spatial and channel dimensions. More recently， Transformer-based models have been applied to change detection， benefiting from self-attention mechanisms to model global dependencies and complex change patterns. Despite these advances， deep learning–based methods still face significant challenges in forest change detection. Bi-temporal remote sensing images often exhibit pronounced style discrepancies caused by seasonal variations， illumination conditions， and sensor differences， which introduce substantial pseudo-change interference. Moreover， forest changes are characterized by high spatial heterogeneity， ranging from subtle local variations to large-scale degradation or recovery processes， resulting in complex multi-scale and multi-pattern change characteristics. These factors make it particularly challenging to suppress style-induced pseudo changes while accurately perceiving and localizing change targets at different spatial scales. To address these challenges， this paper proposes a frequency-enhanced hierarchical interaction network for accurate forest change detection.MethodThis paper proposes a frequency-enhanced hierarchical interaction network for forest change detection in remote sensing imagery. Bi-temporal multispectral images are taken as input and first processed by shared-weight spatial–spectral feature extraction modules to obtain multi-level spatial–spectral representations. To optimize the global representation of bi-temporal features， a frequency feature enhancement module （FFEM） is designed to enhance bi-temporal feature representations. This module performs adaptive modulation in the frequency domain to suppress low-frequency style interference and emphasize mid- and high-frequency structural information， thereby alleviating pseudo-change artifacts caused by imaging condition discrepancies. Subsequently， a cross-temporal correlation-guided interaction module （CCIM） is introduced to explicitly model temporal correlations between bi-temporal images and extract discriminative change features. By leveraging feature interaction and correlation guidance， this module captures temporal dependencies， enabling the extraction of stable change representations and improving the network’s sensitivity to subtle forest changes. In addition， a spatial–spectral coupled interaction module （SCIM） is employed to fully exploit the complementary information among change features at different hierarchical levels. This module jointly models spatial and channel-wise dependencies by integrating fine-grained structural details with high-level semantic representations， thereby enhancing the perception of multi-scale forest change regions. Finally， the fused change features are fed into a classification head to generate the change detection results.ResultComparison experiments were conducted on three forest change detection datasets to compare the proposed method with six advanced approaches， including Spectral-Former， CSANet， TriTF， DIEFEN， FrFTML， and AIWSEN. For each dataset， 10% of the samples were randomly selected for training， while the remaining samples were used for testing. Performance was evaluated using five widely adopted metrics for change detection tasks， including overall accuracy （OA）， Kappa coefficient （KC）， precision （Pre）， recall （Rec）， and F1-score （F1）. The proposed method achieves Kappa coefficients of 0.8102， 0.7355， and 0.7635 on the three datasets， respectively. Compared with the second-best method， it yields performance gains of 1.09%， 2.62%， and 1.74%. In addition， ablation studies conducted on all three datasets further verify the effectiveness of the proposed modules. The ablation experiments indicate that the frequency feature enhancement module， the cross-temporal correlation-guided interaction module， and the spatial–spectral coupled interaction module play key roles in improving change detection accuracy by effectively suppressing pseudo-changes and enhancing the perception of subtle and multi-scale forest changes. The proposed method consistently outperforms mainstream approaches across all datasets. These results highlight the advantage of the proposed design in achieving accurate and stable forest change detection.ConclusionThis paper proposes a frequency-enhanced hierarchical interaction network for forest change detection in remote sensing imagery. By integrating bi-temporal frequency-domain feature enhancement， cross-temporal change extraction， and collaborative interaction among multi-level change features， the proposed method effectively improves forest change detection performance. Experimental results show that the proposed approach consistently outperforms existing mainstream methods across multiple forest change detection datasets， demonstrating strong consistency and stability. Benefiting from the synergistic design of frequency enhancement and hierarchical interaction， the method effectively suppresses style interference caused by bi-temporal imaging condition discrepancies， leading to a significant reduction in pseudo-change false alarms. Meanwhile， in complex forest scenarios， the proposed approach exhibits enhanced capability in perceiving and localizing subtle change regions as well as change objects at different spatial scales， enabling accurate identification of both local structural variations and large-scale evolutionary processes. These results indicate that the proposed method achieves strong robustness and applicability under complex remote sensing imaging conditions， providing a reliable solution for forest change monitoring and related applications.

ObjectiveWith the rapid advancement and widespread proliferation of facial forgery technologies， social issues such as the dissemination of misinformation and identity fraud have become increasingly severe. Regarding existing forgery detection methods based on face image reconstruction， most employ multi-task learning to reconstruct either real or fake faces in isolation， failing to explicitly magnify the reconstruction discrepancies between the two. Consequently， the detection accuracy and generalization performance of these network models are limited. To address these issues， a method called “differential reconstruction-driven residual guidance for face forgery detection” is proposed.MethodFirstly， to explicitly magnify the differences between real and fake faces during the image reconstruction process， a contrastive differential reconstruction network （CDRNet） is introduced. This method constructs clear and blurred image reconstruction targets for real and fake faces， respectively， guiding the network to focus on the high-frequency regions of faces with obvious forgery traces during reconstruction， thereby enhancing the overall model's ability to distinguish between real and fake faces. Secondly， addressing the issue that existing detection methods fail to fully exploit the guidance information from reconstructed residual maps， a residual dual-domain guiding module （RDDGM） is designed. This module deeply integrates spatial and high-frequency domain features， utilizing the reconstructed residual information to guide the fused dual-domain features， thereby enhancing the network model's capability to capture subtle forgery traces. In addition， in order to enable the model to learn the common forgery features among different forgery methods， a text-aware loss module （TALM） is introduced. Through the guidance of text modal information， the results of contrastive differential reconstruction are further optimized， and the generalization performance of the network model for unknown forgery methods is significantly improved. The main contributions of this paper are summarized as follows： 1） In order to explicitly magnify the differences between real and fake faces before and after reconstruction， the contrastive differential reconstruction network （CDRNet） was designed to construct differential reconstruction targets for real and fake faces respectively. The model's ability to distinguish between real and fake human faces has been enhanced. 2） In order to enhance the ability of the network model to capture subtle forgery traces， the residual dual-domain guidance module （RDDGM） is proposed， which uses the reconstructed residual information to guide the fusion features of the high-frequency domain and the spatial domain of the image. Fully explore the subtle forgery traces in the image domain and the high-frequency domain. 3） In order to enhance the generalization ability of the network model for unknown forgery methods， the text-aware loss module （TALM） is proposed. Text modal information is introduced to further optimize and compare the differential reconstruction results， promoting the model to learn the common forgery features among various forgery methods. The generalization ability of the network model has been significantly enhanced. 4） Experimental results demonstrate that the proposed method achieves highly competitive performance in both in-domain and cross-domain evaluations across multiple datasets， including FaceForensics++ （FF++）， Celeb-DF V1 and V2， DeepFake Detection Challenge （DFDC）， DeepFake Detection Challenge Preview （DFDCP）， and Deepfake Detection （DFD）. Based on the Dlib library， this paper extracts 32 facial frames from each video in the training set and 64 frames from each video in the test set. All images are uniformly resized to 224×224， and their pixel values are normalized to before being fed into the network. In terms of evaluation metrics， following prior research， this study primarily adopts accuracy （ACC） and area under the ROC curve （AUC） to assess network performance. Experimental verification confirms that the AdamW optimizer is used for training the network model， with the initial learning rate and batch size set to 1E-4 and 8， respectively. The weights of the image encoder are initialized using an EfficientNet-B4 model pre-trained on ImageNet. The radius of the filter frequency domain mask in CDRNet is set to 16. During training， multiple data augmentation strategies are employed， including random horizontal flipping， small-angle rotation， random cropping， scaling， and color jittering. The proposed method is implemented based on the PyTorch framework， and the model is trained using a single NVIDIA GeForce RTX 2080Ti GPU.ResultIn intra-domain experiments， compared with the best-performing comparison method， the proposed method improves ACC and AUC by 2.83% and 1.75%， respectively， thereby demonstrating superior performance in identifying forgeries within the same data distribution. In cross-domain experiments， the method was rigorously tested on 5 public datasets and compared with 13 typical methods， achieving a 1.75% improvement in average AUC. Comprehensive ablation studies further demonstrate that the proposed modules， including CDRNet， RDDGM， and TALM， all significantly contribute to enhancing the overall detection performance and generalization of the face forgery detection model.ConclusionThis work introduces a novel integration of contrastive learning and image differential reconstruction for face forgery detection， substantially improving the model's generalization accuracy on unseen forgery methods. Specifically， the CDRNet amplifies the discrepancies between real and forged faces during the reconstruction process to bolster discriminative capability， while the RDDGM leverages reconstruction residual information to guide dual-domain feature fusion， thereby improving the perception of subtle forgery traces. Furthermore， the TALM introduces text modality information to learn generic forgery features across different methods， enhancing generalization to unknown forgeries. Although experimental results demonstrate that the proposed method outperforms existing approaches in both in-domain and cross-domain scenarios， it currently faces challenges regarding inference efficiency due to model complexity， and the use of fixed text prompts limits the full potential of TALM's cross-modal guidance. Consequently， future work will focus on lightweight network optimization and the construction of a dynamic text prompt library to strengthen cross-modal correlations， aiming to further improve the model's efficiency and practicality.