Scrambling parameter generation to improve perceptual information hiding

The present study proposes the method to improve the perceptual information hiding in image scramble approaches. Image scramble approaches have been used to overcome the privacy issues on the cloud-based machine learning approach. The performance of image scramble approaches are depending on the scramble parameters; because it decides the performance of perceptual information hiding. However, in existing image scramble approaches, the performance by scrambling parameters has not been quantitatively evaluated. This may be led to show private information in public. To overcome this issue, a suitable metric is investigated to hide PIH, and then scrambling parameter generation is proposed to combine image scramble approaches. Experimental comparisons using several image quality assessment metrics show that Learned Perceptual Image Patch Similarity (LPIPS) is suitable for PIH. Also, the proposed scrambling parameter generation is experimentally confirmed effective to hide PIH while keeping the classification performance.