Covert Communication and Image Authentication Algorithm Based on Adversarial Examples

Qiwen Wu, Zijing Feng, Yingkai Huang, Jiehui Zhong, Xiaolong Liu
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Abstract

The research on the application of adversarial examples mainly focuses on considering adversarial examples as a threat in the past. In order to make better use of the adversarial examples, rather than just taking it as the defects of neural network, a covert communication method based on adversarial examples is proposed in this paper. Using the characteristics that adversarial examples can carry information, we combine it with specific coding rules to develop a covert communication algorithm. Unlike the traditional steganography, the secret information is not contained in the communication content of the sender and the receiver itself in the proposed scheme. The mapping relationship between adversarial examples and secret information is hidden in the neural network model, so as to realize the hidden transmission of information and improve the concealment and security of communication. At the same time, the tamper identification is embedded in the tensor of adversarial output. When the image is modified during transmission, the tamper identification will also change, so that the image can be authenticated. Experiments show the feasibility of the algorithm and verify that it can completely extract secret information from the encrypted image adversarial example and authenticate the integrity of the image.
基于对抗性示例的隐蔽通信和图像认证算法
过去对对抗性样例应用的研究主要集中在将对抗性样例视为一种威胁。为了更好地利用对抗样例,而不是将其视为神经网络的缺陷,本文提出了一种基于对抗样例的隐蔽通信方法。利用对抗性示例可以携带信息的特点,我们将其与特定的编码规则相结合,开发了一种隐蔽通信算法。与传统的隐写技术不同,该方案中的秘密信息不包含在发送方和接收方本身的通信内容中。将对抗实例与秘密信息的映射关系隐藏在神经网络模型中,从而实现信息的隐式传输,提高通信的隐蔽性和安全性。同时,将篡改识别嵌入到对抗输出的张量中。当图像在传输过程中被修改时,篡改者标识也会发生变化,从而可以对图像进行认证。实验证明了该算法的可行性,并验证了该算法能够完全从加密图像对抗示例中提取秘密信息,验证图像的完整性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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