Generative Steganography Based on GANs

Traditional steganography algorithms embed secret information by modifying the content of the images, which makes it difficult to fundamentally resist the detection of statistically based steganalysis algorithms. To solve this problem, we propose a novel generative steganography method based on generative adversarial networks. First, we represent the class labels of generative adversarial networks in binary code. Second, we encode the secret information into binary code. Then, we replace the labels with the secret information as the driver to generate the encrypted image for transmission. Finally, we use the auxiliary classifier to extract the label of the encrypted image and obtain the secret information through decoding. Experimental results and analysis show that our method ensures good performance in terms of steganographic capacity, anti-steganalysis and security.

[1]  Cheng-Hsing Yang,et al.  Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems , 2008, IEEE Transactions on Information Forensics and Security.

[2]  Jessica J. Fridrich,et al.  Designing steganographic distortion using directional filters , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[3]  Francis M. Boland,et al.  Phase watermarking of digital images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  Zhenxing Qian,et al.  Robust Steganography Using Texture Synthesis , 2017 .

[5]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[6]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[7]  Ingemar J. Cox,et al.  Secure spread spectrum watermarking for multimedia , 1997, IEEE Trans. Image Process..

[8]  Pingzhi Fan,et al.  An Efficient Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization , 2008, IEEE Transactions on Multimedia.

[9]  Kuo-Chen Wu,et al.  Steganography Using Reversible Texture Synthesis , 2015, IEEE Transactions on Image Processing.

[10]  Xingming Sun,et al.  Coverless Information Hiding Method Based on the Chinese Mathematical Expression , 2015, ICCCS.

[11]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[12]  Xiaogang Jin,et al.  Hidden message in a deformation-based texture , 2014, The Visual Computer.

[13]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[14]  Tomás Pevný,et al.  Using High-Dimensional Image Models to Perform Highly Undetectable Steganography , 2010, Information Hiding.

[15]  Xingming Sun,et al.  Coverless Image Steganography Without Embedding , 2015, ICCCS.

[16]  Jessica J. Fridrich,et al.  Universal distortion function for steganography in an arbitrary domain , 2014, EURASIP Journal on Information Security.