When Perceptual Authentication Hashing Meets Neural Architecture Search

In recent years, many perceptual authentication hashing schemes have been proposed, especially for image content authentication. However, most of the schemes directly use the dataset of image processing during model training and evaluation, which is actually unreasonable due to the task difference. In this paper, we first propose a specialized dataset for perceptual authentication hashing of images (PAHI), and the image content-preserving manipulations used in this dataset are richer and more in line with realistic scenarios. Then, in order to achieve satisfactory perceptual robustness and discrimination capability of PAHI, we exploit the continuous neural architecture search (NAS) on the channel number and stack depth of the ConvNeXt architecture, and obtain two PAHI architectures i.e., NASRes and NASCoNt. The former has better overall performance, while the latter is better for some special manipulations such as image cropping and background overlap. Experimental results demonstrate that our architectures both can achieve competitive results compared with SOTA schemes, and the AUC areas are increased by 1.6 (NASCoNt) and 1.7 (NASRes), respectively.