Performing post-processing on the stego image has promise for improving the steganography security. Nevertheless, the existing post-processing schemes neglect the characteristics of the stego image, which lack strong theoretical interpretability. Moreover, existing schemes do not fully consider the holistic steganography security against both CNN-based and handcrafted steganalyzers. In this paper, we propose a steganography immunoprocessing (IP) framework based on Artificial Immune System (AIS) that is universal for the stego images from the same steganographic process to further enhance the security. Based on the natural relationship between immune theory and steganography, we regard the immunoprocessing policy as the antibody, and the performance of the anti-steganalysis for stego images protected by antibody as the antibody affinity. By the immune dynamic optimization process, the optimal immunoprocessing policy is dynamically searched and performed on the stego image to achieve further optimization. In addition, we enhance the resistance of the stego against the target CNN-based steganalyzer by limiting the immunoprocessing direction. Performing the optimal immunoprocessing on stego images will enhance the holistic security of steganography. Experimental results demonstrate that the proposed immunoprocessing can significantly improve the holistic security of adaptive steganography against both CNN-based and handcrafted steganalyzers, and achieve better performance than related schemes.

中文翻译：

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针对基于 CNN 和手工隐写分析的隐写术免疫处理框架


对隐写图像进行后处理有望提高隐写术的安全性。然而，现有的后处理方案忽略了隐写图像的特征，缺乏较强的理论解释性。此外，现有方案没有充分考虑针对基于 CNN 和手工制作的隐写分析器的整体隐写安全性。在本文中，我们提出了一种基于人工免疫系统（AIS）的隐写免疫处理（IP）框架，该框架对于来自同一隐写过程的隐写图像是通用的，以进一步增强安全性。基于免疫理论和隐写术之间的天然关系，我们将免疫处理策略视为抗体，将受抗体保护的隐写图像的反隐写分析性能视为抗体亲和力。通过免疫动态优化过程，动态搜索最优免疫处理策略并在隐写图像上执行，以实现进一步的优化。此外，我们通过限制免疫处理方向来增强隐写对基于 CNN 的目标隐写分析器的抵抗力。对隐写图像进行最佳的免疫处理将增强隐写术的整体安全性。实验结果表明，所提出的免疫处理可以显着提高自适应隐写术针对基于 CNN 和手工隐写分析器的整体安全性，并获得比相关方案更好的性能。