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SIA-GAN: Scrambling Inversion Attack Using Generative Adversarial Network
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DOI：10.1109/ACCESS.2021.3112684.png)
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摘要
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This paper presents a scrambling inversion attack using a generative adversarial network (SIA-GAN). This method aims to evaluate the privacy protection level achieved by image scrambling method. For privacy-preserving machine learning, scrambled images are often used to protect visual information, assuming that searching the scramble parameters is highly difficult for an attacker due to the application of complex image scrambling operations. However, the security of such methods has not been thoroughly investigated. SIA-GAN learns the mapping between pairs of scrambled images and original images, then attempts to invert image scrambling. Therefore, the attacker is assumed to have real images whose domain is the same as that of scrambled images. Experimental results demonstrate that scrambled images cannot be recovered if block shuffling is applied as a scrambling operation. The experimental code of SIA-GAN is available at https://github.com/MADONOKOUKI/SIA-GAN.
Keyword:
Training
Feature extraction
Visualization
Generators
Generative adversarial networks
Transforms
Machine learning
Artificial intelligence
machine learning
computer vision
visual information hiding
image scrambling
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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IEEE ACCESSIF3.6
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