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Received：22 June 2021，
Revised：2021-10-12，
Accepted：19 October 2021，
Published：16 January 2022
移动端阅览
目的
2
由于空域图像下采样过程中提供的量化误差边信息能够有效提升隐写安全性，为了得到下采样之前的高分辨率图像，提出一种基于超分辨率网络的空域图像边信息估计隐写方法。
方法
2
受原始下采样边信息隐写方法的启发，使用超分辨率网络生成被称为预载体的高分辨率图像。同时利用现有的空域图像对称失真算法得到每个像素点的修改失真，然后以浮点型精度对预载体下采样，得到和载体同分辨率的图像形式，利用对应像素点间的差值指导像素点的修改方向，实现基于初始失真的非对称失真调整。首先以峰值信噪比和极性估计准确率为指标对比了多种超分辨率网络以及基于传统插值方法的上采样性能，并通过调整初始失真分别进行隐写和隐写分析实验，选择使安全性提升最大的残差通道注意力机制网络及其对应调整系数作为本文的下采样边信息估计隐写方法。
结果
2
使用隐写领域中常用的3个数据库、两种传统初始失真函数以及两类隐写分析方法进行实验。在跨数据集的隐写安全性上
相比传统隐写方法，在对抗基于手工特征和基于深度学习的隐写分析时，本文方法的安全性均有显著提升，如在测试集载体图像上，嵌入率为0.5 bit/像素时，安全性分别提升6.67 %和6.9 %；在训练集载体图像上，本文方法的安全性在比传统方法有很大提升的基础上，甚至在一些情况下能够高于原始边信息隐写方法的安全性，如在对抗基于手工特征的隐写分析器且嵌入率为0.1 bit/像素时，安全性提升1.08 %；在对抗基于深度学习的隐写分析器且嵌入率为0.5 bit/像素时，安全性提升0.6 %。
结论
2
实验表明，使用超分辨率网络作为下采样边信息估计的工具，并利用估计边信息调整嵌入修改的初始失真，能够有效提升传统隐写方法的安全性，并接近甚至在部分情况下超越了原始边信息隐写的安全性。除此之外，本文方法与原始边信息隐写方法具有不同的修改模式，而且具有更广泛的适用性。
Objective
2
Steganography is a way of covert communication to achieve the transmission of a secret message via slight modification of the elements on the cover images without causing suspicion of the steganalysis. Security is capable to embed the secret message with minimal distortion via syndrome-trellis codes (STC)
steganographic polar codes (SPC) or optimal analogue embedding. The embedding rate and loss function is demonstrated. The initial symmetric distortions function assigns the same cost for the modification of pixel values ±1. Some adapting methods on top of symmetric distortion have also been generated
demonstrating the effectiveness of asymmetric distortion steganography for improving steganographic security. To improve steganography security
the prompted guidance of the adjustment of the initial cost and previous work has proved that the quantization error information provided via image downsampling used as auxiliary information Steganography does not have the original image before downsampling in many real scenes. For computer vision
super-resolution tasks are rapidly evolving
which can end-to-end generate high-resolution images corresponding to low-resolution images. In order to get the high-resolution images before downsampling based on the downsampled side information
this research has proposed a steganography based on super-resolution networks for estimating side information of spatial domain images. The unique side information provided by the estimated high-resolution images in the downsampling process can effectively improve steganography security excluding high scaling.
Method
2
Based on the initial side information steganography method
the steganographer cover for embedding is obtained via various image processing processes in common
such as the downsampling process involved. A pre-cover downsampling image has been called for obtained cover. This research has briefly proposed some relevant super-resolution networks to assess the quality of the resulting image via peak signal to noise ratio (PSNR) and polarity estimation correctness initially. The network that can make the largest contribution to stenographic security is opted for the first step of estimating the side information
i.e.
generating a high-resolution precover image. Current side information estimated steganography methods in the context of JPEG image steganography has derived their side information from the quantization error generated in the JPEG compression process. The cost effective strategy uses the degradation model of the original side information steganography
i.e.
the solo polarity of the error is considered without the magnitude of the error. The modification cost of the pixel points is in the same scale. The current loss function in the spatial domain is used to obtain the cost of modification for each pixel. The image form with the same resolution as the cover is acquired via floating-point precision of the pre-cover downsampling. Based on the initial cost
the differentiation amongst the corresponding pixels is used to guide the modification of the pixels to achieve asymmetric distortion adjustment. A steganographic framework for side information has been estimated in spatial domain images based on super-resolution networks.
Result
2
The initial demonstration compare the degree of improvement in steganographic security for side information estimated based on various super-resolution networks. The residual channel attention network (RCAN) with a scaling of 2 as the side information estimating network model is illustrated at last. The optimal cost adjustment coefficients is experimentally obtained at different embedding rates. Three databases including break our steganographic system (BOSSBase)
break our watermarking system 2 (BOWS2) and mixed resized never-compressed (MRNC) and two initial distortion functions in the context of high-pass
low-pass
and low-pass (HILL) and unIversal wavelet relative distortion for the spatial (SUNIWARD) are used to test the security of the demonstrated method against manual feature and network steganalysis. In terms of cross-database steganographic security
such as BOWS2
the security method is significantly improved against spatial rich model (SRM) and steganalysis residual network (SRNet) steganalysis compared with the original method HILL. When the embedding rate is 0.5 bit/pixel
the demonstrated method improves 6.67 % and 6.9 %
respectively. The embedding rate is 0.1 bit/pixel based on the improvement of 1.74 % and 5.8 % each. Meanwhile
this analysis improves 4.04 % and 4.0 %
respectively
over the two traditional steganography methods on cover images set that are not directly derived from the downsampling process. The difference has been shown and the original side information steganography is compared on the training set cover in terms of steganographic security and modification point distribution. Both side information estimated steganography and original side information steganography greatly improve steganographic security
but their modification modes are different. The initial distortion is calculated with the HILL and the embedding rate is 0.1 bit/pixel in particular. The security of the proposed method exceeds that of original side information steganography method by 1.08 % against SRM steganalysis. The embedding rate is 0.5 bit/pixel. The security of the proposed method exceeds that of original side information steganography by 0.6 % against SRNet steganalysis. It is illustrated that the analyzed method has its priority on the steganography of initial side information steganography in some cases.
Conclusion
2
The estimated downsampled side information has been proposed first to adjust the initial cost of modified pixels in order to distinguish the modified loss of pixel points in different directions for asymmetric distortion steganography. To obtain effective auxiliary information
a super-resolution network to estimate the corresponding high-resolution image of the cover has been proposed simultaneously. The cost adjustment integrated strategy is improving the steganographic security effectively. Compared with original side information steganography
the research priority is that it can be widely applied to steganography of multi-sources cover images while original side information steganography cannot be applied
i.e.
the original high-resolution image cannot be obtained
and has a wider application scenario than original side information steganography. In addition
the research method can be applied to the field of JPEG domain. Limitation there is still a certain gap based on the illustrated security method in most scenarios where both methods can be applied. The estimated side information steganography method has been developing more suitable network structures and cost modification strategies further.
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