基于嵌入代价学习和边信息估计的JPEG隐写算法
JPEG Steganography with Embedding Cost Learning and Side-Information Estimation
摘要：
基于卷积神经网络（CNN）的隐写分析方法的广泛应用给隐写术带来了巨大的挑战。为此，基于生成对抗网络（GANs）的嵌入成本学习框架已经被提出，并在空间隐写中取得了成功。然而，GAN在JPEG隐写中的应用还处于原型阶段，其抗检测能力和训练效率有待提高。在传统的隐写术中，研究表明，从预覆盖计算的边信息可以用于增强安全性。然而，如果没有空间域图像，则很难计算边信息。本文提出了一种基于生成对抗网络的JPEG隐写算法的嵌入代价学习框架（JS-GAN），学习后的嵌入代价可以根据边信息进行非对称调整。实验结果表明，该方法能自动学习自适应的嵌入代价函数，并合理利用估计的边信息，有效提高安全性能。例如，在质量因子为75和0.4 bpnzAC的经典隐写分析器GFR的攻击下，所提出的JS-GAN可以比J-UNIWARD增加检测错误2.58%，并且估计的边信息辅助版本JS-GAN（ESI）可以比JS-GAN进一步提高安全性能11.25%。
英文摘要：
A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) have been proposed and achieved success for spatial steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its anti-detectability and training efficiency should be improved. In conventional steganography, research has shown that the side-information calculated from the precover can be used to enhance security. However, it is hard to calculate the side-information without the spatial domain image. In this work, an embedding cost learning framework for JPEG Steganography via a Generative Adversarial Network (JS-GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side-information. Experimental results have demonstrated that the proposed method can automatically learn a content-adaptive embedding cost function, and use the estimated side-information properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with quality factor 75 and 0.4 bpnzAC, the proposed JS-GAN can increase the detection error 2.58% over J-UNIWARD, and the estimated side-information aided version JS-GAN(ESI) can further increase the security performance by 11.25% over JS-GAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
