基于对抗攻击的图像隐写策略搜索

传统的隐写方法依赖于难以构建的复杂的人工规则。基于富特征模型和深度学习的隐写分析方法击败了现有最优的隐写方法，这使得隐写的安全性面临挑战。为此提出了一种基于对抗攻击的图像隐写策略的搜索方法，以寻找合适的隐写策略。隐写模型首先根据已知隐写算法初始化失真代价，然后建立含参的代价调整策略。对手模型区分载体和载密图像的分布，以发现潜在的隐藏行为。针对对手模型，利用定向对抗攻击得到相应的基于梯度符号的评价向量。在隐写模型与对手模型之间建立对抗博弈过程，据此搜索目标隐写策略。隐写模型和对手模型均用深度神经网络模型实现。构建了4种隐写配置并同3种隐写方法进行了实验比较。结果表明，该方法能有效搜索到图像隐写策略，与人工设计的经典方法和最新的隐写方法相比具有竞争力。

Search the Steganographic Policy for Image via Adversarial Attack

Steganography is to conceal the presence of secret communication. Traditional steganographic schemes rely on complex artificial rules that are difficult to construct. Steganalysers based on the rich models and deep learning achieve state-of-the-art performance. The security performance of existing steganographic methods is being challenged. In this paper, a search method based on image steganography model against attack is proposed to find a suitable steganography policy. The steganographic model constructs the parametric policy. The adversary model distinguishes the distribution of stego from cover to find the potential hiding artefacts. To obtain the corresponding evaluations, the adversarial attack is performed on adversary model. The security game between steganographic part and adversary is established via corresponding information, thus finding the target steganographic policy. The steganographic model and adversary model are implemented as deep neural networks. On the data set Bossbase, the payload is 0.2 and 0.4 bpp, the steganalysers are SRM and maxSRMd2. Four configurations with three steganographic schemes are compared. The experimental results show that the scheme proposed in this paper can obtain effective policy for image steganography, and the security performance is competitive compared with these schemes.