Secure Steganographic Cover Generation via a Noise-Optimization Stacked StyleGAN2
Author:
Yu Jiang1, Zhou Xiaoyi1, Si Wen1, Li Fengyong2ORCID, Liu Cong1, Zhang Xinpeng3
Affiliation:
1. School of Information and Computer, Shanghai Business School, Shanghai 201499, China 2. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201399, China 3. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Abstract
Recently, the style-based generative adversarial network StyleGAN2 yields state-of-art performance on unconditional high-quality image synthesis. However, from the perspective of steganography, the image security is not guaranteed during the image synthesis. Relying on the optimal properties of StyleGAN2, this paper proposes a noise-optimization stacked StyleGAN2 named NOStyle to generate the secure and high-quality cover (image used for data hiding). In our proposed scheme, we decompose the image synthesis into two stages with symmetrical mode. In stage-I, StyleGAN2 is preserved to generate a high-quality benchmark image. In the stage-II generator, based on the progressive mechanism and shortcut connection, we design a noise secure optimization network by which the different-scale stochastic variation (noise map) is automatically adjusted according to the results of the stage-II discriminator. After injecting the stochastic variation into different resolutions of the synthesis network, the stage-II generator obtains an intermediate image. For the symmetrical stage-II discriminator, we combine the image secure loss and fidelity loss to construct the noise loss which is used to evaluate the difference between two images generated by the stage-I generator and stage-II generator. Taking the outputs of stage-II discriminator as inputs, by iteration, the stage-II generator finally creates the optimal image. Extensive experiments show that the generated image is not only secure but high quality. Moreover, we make a conclusion that the security of the generated image is inverse proportion to the fidelity.
Funder
Natural Science Foundation of Shanghai Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security
Subject
Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)
Reference37 articles.
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