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An Image Style Transfer Network Using Multilevel Noise Encoding and Its Application in Coverless Steganography

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School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
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Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan
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Author to whom correspondence should be addressed.
Symmetry 2019, 11(9), 1152; https://doi.org/10.3390/sym11091152
Received: 17 July 2019 / Revised: 6 September 2019 / Accepted: 8 September 2019 / Published: 11 September 2019
Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. A coverless steganographic approach without any modification for transmitting secret color image is proposed. We propose a diversity image style transfer network using multilevel noise encoding. The network consists of a generator and a loss network. A multilevel noise to encode matching the subsequent convolutional neural network scale is used in the generator. The diversity loss is increased in the loss network so that the network can generate diverse image style transfer results. Residual learning is introduced so that the training speed of network is significantly improved. Experiments show that the network can generate stable results with uniform texture distribution in a short period of time. These image style transfer results can be integrated into our coverless steganography scheme. The performance of our steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness. View Full-Text
Keywords: convolutional neural networks; coverless steganography; image style transfer; multilevel noise encoding convolutional neural networks; coverless steganography; image style transfer; multilevel noise encoding
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Zhang, S.; Su, S.; Li, L.; Zhou, Q.; Lu, J.; Chang, C.-C. An Image Style Transfer Network Using Multilevel Noise Encoding and Its Application in Coverless Steganography. Symmetry 2019, 11, 1152.

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