In this paper, we present a novel adversarial embedding scheme named Steganalytic Feature based Adversarial Embedding (SFAE), which is elaborately designed in a non-data-driven style. Firstly, a novel DCTR based adversary is designed to generate adversarial stego images which can not only resist feature based steganalysis but also deep learning based steganalysis. Specifically, our adversary consists of an end-to-end neural network structure, while its inner weights are set according to DCTR rather than learned from datasets. Secondly, we use the minimum distance to the cover in steganalytic space as the criterion to select the optimal adversarial stego image, rather than fooling the adversary. Last but not least, we present two SFAE implementations to adapt to different cases. One is Iterative SFAE, which needs to calculate gradients multiple times. Iterative SFAE is more secure but has higher complexity. It fits the case that the steganographer has adequate computing resources. Another implementation is Oneshot SFAE, which can calculate gradients once. Oneshot SFAE trades the security for lower complexity. It fits the steganographer that has stricter requirements for running time. Experiments demonstrate that SFAE is effective to improve the security of conventional steganographic schemes against the state-of-the-art steganalysis including both feature based steganalysis and deep learning based steganalysis.

在本文中，我们提出了一种新颖的对抗嵌入方案，称为基于隐写特征的对抗嵌入（SFAE），该方案以非数据驱动的方式精心设计。首先，设计了一种基于DCTR的新型对手，以生成对抗性隐身图像，该图像不仅可以抵抗基于特征的隐身分析，而且可以抵抗基于深度学习的隐身分析。具体来说，我们的对手由端到端的神经网络结构组成，而其内部权重是根据DCTR设置的，而不是从数据集中学习的。其次，我们以隐身分析空间中与掩体之间的最小距离作为选择最佳对抗性隐身图像的准则，而不是愚弄对手。最后但并非最不重要的一点是，我们提出了两种SFAE实现以适应不同情况。一种是迭代SFAE，需要多次计算梯度。迭代SFAE更安全，但复杂度更高。隐写术者具有足够的计算资源的情况很合适。另一种实现是Oneshot SFAE，它可以计算一次梯度。Oneshot SFAE将安全性换成了较低的复杂性。它适合对运行时间有更严格要求的隐身术师。实验表明，SFAE可以有效地提高常规隐写方案的安全性，以对抗包括基于特征的隐写分析和基于深度学习的隐写分析在内的最新隐写分析。Oneshot SFAE将安全性换成了较低的复杂性。它适合对运行时间有更严格要求的隐身术师。实验表明，SFAE可以有效地提高常规隐写方案的安全性，以对抗包括基于特征的隐写分析和基于深度学习的隐写分析在内的最新隐写分析。Oneshot SFAE将安全性换成了较低的复杂性。它适合对运行时间有更严格要求的隐身术师。实验表明，SFAE可以有效地提高常规隐写方案的安全性，以对抗包括基于特征的隐写分析和基于深度学习的隐写分析在内的最新隐写分析。