Social media and cloud computing have facilitated the distribution and storage of images. Meanwhile, concerns about image privacy have been raised. It is crucial to protect image privacy from privacy violation and illegal use. Motivated by this, an image privacy protection method based on cycle-consistent generative adversarial networks (CycleGAN) is proposed in this paper. Considering the usability in image privacy protection, the method first combines image segmentation with CycleGAN to select different segmentation coefficients to generate images with different degrees of privacy protection. Then reversible information hiding is used to embed information in the generated privacy preserving image, so as to prevent illegal users from reversing the image. Thus, a balance is achieved between image privacy protection and usability. The proposed method is trained and tested using PIPA dataset, using peak signal to noise ratio and structural similarity index are used as performance metrics to evaluate the privacy-preserving images. Experimental results show that the proposed method outperforms other schemes in both image privacy preservation and usability.
XIE Yiyi, ZHANG Yushu, ZHAO Ruoyu, WEN Wenying, ZHOU Yuqian
. Image Privacy Protection Based on Cycle-Consistent Generative Adversarial Networks[J]. Journal of Applied Sciences, 2023
, 41(2)
: 228
-239
.
DOI: 10.3969/j.issn.0255-8297.2023.02.004
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