Block-wise Scrambled Image Recognition Using Adaptation Network
This addresses security concerns for image data by preventing unauthorized machine recognition, though it appears incremental as it builds on existing DNN methods.
The paper tackled the problem of generating images that are recognizable by humans but not by machines, using block-wise scrambling to hide perceptual information and an adaptation network for recognition. Experimental results on CIFAR datasets showed that the adaptation network effectively integrated simple perceptual hiding into DNN-based classification.
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.