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.
翻译:由于广泛应用基于进化神经网络(CNNs)的分层分析方法,对摄制法提出了巨大的挑战。为此,提议了基于基因对抗网络(GANs)的嵌入成本学习框架,并取得了空间切片学的成功。然而,将GAN应用于JPEG的切片学仍处在原型阶段;其防探测性和培训效率应得到改善。在常规的分层法中,研究显示,从预先覆盖中计算的侧信息可用于加强安全。然而,在不使用空间域图图像的情况下,很难计算侧面信息。在这项工作中,提议通过Generation Adversarial网络(JS-GAN)嵌入成本学习框架,但是,对GPEGEG的嵌入成本应用仍然处于原型阶段;其反探测能力和培训效率应得到改善。实验结果表明,拟议的方法可以自动学习内容调整嵌入成本功能,并且使用估计的侧面信息可以有效地改进JSAN-AN-MA-MAS-MAS-MAS-BS-MAR)的绩效。例如,在SMA-MA-G-G-G-G-MANS-R-R-R-R-R-R-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-SA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA