A Vision Transformer trained on key-scrambled medical images achieves 94% validation accuracy and appears robust to simple noise and reconstruction attacks.
Block-wise Scrambled Image Recognition Using Adaptation Network
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In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines. Hence, both the perceptual information hiding and the corresponding object recognition methods should be developed. Block-wise image scrambling is introduced to hide perceptual information from a third party. In addition, an adaptation network is proposed to recognize those scrambled images. Experimental comparisons conducted using CIFAR datasets demonstrated that the proposed adaptation network performed well in incorporating simple perceptual information hiding into DNN-based image classification.
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AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach
A Vision Transformer trained on key-scrambled medical images achieves 94% validation accuracy and appears robust to simple noise and reconstruction attacks.