On Security Enhancement of Steganography via Generative Adversarial Image

Abstract

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.