SSGAN: Secure Steganography Based on Generative Adversarial Networks

Abstract

In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.

References in corpus (3)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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• Generative Adversarial Text to Image Synthesis

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• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
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• Invisible Steganography via Generative Adversarial Networks
• Recent Advances of Image Steganography with Generative Adversarial Networks
• Weakening the Detecting Capability of CNN-based Steganalysis
• Digital Cardan Grille: A Modern Approach for Information Hiding
• The Reincarnation of Grille Cipher: A Generative Approach
• Generative Steganography by Sampling
• GANash -- A GAN approach to steganography
• Multi-Stage Residual Hiding for Image-into-Audio Steganography