The effectiveness of deep learning-based steganalyzers is significantly compromised by adversarial steganography. In response to this challenge, recent efforts have been devoted to identifying distinct traces of adversarial perturbations, yet they have overlooked the inherently adversarial robustness required in steganalyzers. This paper aims to develop a steganalytic model that defends against adversarial steganography by increasing the difficulty of generating adversarial stego images. To achieve this objective, the techniques of learning neighboring feature relationships and self-adversarial adjustment are proposed with three essential modules. The first one, named K-times Dropout Neighboring Feature Transformer (KDNFT), is designed to accept a set of neighboring features obtained by dropout as input. Based on the finding that K-times dropout neighboring features have different distributions for covers and adversarial stegos, KDNFT effectively learns to exploit the relationships among these features for adversarial steganalysis. To facilitate adversarial training, which is an effective way to improve intrinsic robustness, the second module called Pseudo Adversarial Stego Generator (PASG) is proposed to synthesize samples for training. The third module is a Test-time Active Perturbation (TAP) module that adjusts the results of adversarial stego samples close to the decision boundary in a self-adversarial way. Extensive experiments demonstrate that our method achieves improvements in steganalyzing various kinds of adversarial steganographic methods.

中文翻译：

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通过学习相邻特征关系和自对抗调整构建本质鲁棒 Steganalyzer


基于深度学习的隐写分析器的有效性受到对抗性隐写术的严重影响。为了应对这一挑战，最近的努力致力于识别对抗性扰动的不同痕迹，但他们忽视了隐写分析器所需的固有对抗稳健性。本文旨在开发一个隐写分析模型，通过增加生成对抗性隐写图像的难度来防御对抗性隐写术。为了实现这一目标，提出了学习相邻特征关系和自我对抗调整的技术，其中包含三个基本模块。第一个名为 K-times Dropout Neighboring Feature Transformer （KDNFT），旨在接受通过 dropout 获得的一组相邻特征作为输入。基于 K-times dropout 相邻特征对封面和对抗性隐写具有不同分布的发现，KDNFT 有效地学会了利用这些特征之间的关系进行对抗性隐写分析。为了促进对抗性训练，这是提高内在鲁棒性的有效方法，提出了第二个模块，称为伪对抗性隐写生成器（PASG），用于合成样本进行训练。第三个模块是测试时主动扰动 （TAP） 模块，它以自对抗的方式调整接近决策边界的对抗性隐写样本的结果。广泛的实验表明，我们的方法在隐写分析各种对抗性隐写法方法方面取得了改进。