Steganography plays an important role in information hiding. With the development of steganalysis, traditional steganography faces more detection threat. It is necessary to improve security of current steganographic methods. One effective way is to generate suitable covers for steganography, which can be achieved by adversarial learning. In this letter, we propose a new approach for quickly constructing high-quality adversarial images. Compared with original images, the generative adversarial images are more suitable for carrying secret information. According to the characteristics of steganography, we design a new loss function in adversarial attacks, which makes the adversarial images obtain the similar classification results before and after steganography. In addition, to further improve security of the adversarial images, we also make use of the zero-sum idea of generative adversarial networks. Experimental results show that the proposed method can significantly enhance security of steganography.

中文翻译：

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基于生成对抗图像的隐写术安全性增强

隐写术在信息隐藏中起着重要作用。随着隐写分析的发展，传统的隐写术面临更多的检测威胁。有必要提高当前隐写方法的安全性。一种有效的方法是为隐写术生成合适的封面，这可以通过对抗性学习来实现。在这封信中，我们提出了一种快速构建高质量对抗性图像的新方法。与原始图像相比，生成对抗图像更适合携带秘密信息。根据隐写术的特点，我们设计了一种新的对抗性攻击损失函数，使对抗性图像在隐写术前后获得相似的分类结果。此外，为了进一步提高对抗性图像的安全性，我们还利用了生成对抗网络的零和思想。实验结果表明，所提出的方法可以显着提高隐写术的安全性。