End-to-end privacy-preserving image retrieval in cloud computing via anti-perturbation attentive token-aware vision transformer

Abstract

Privacy-Preserving Image Retrieval (PPIR) has gained popularity among users who upload encrypted personal images to remote servers, enabling image retrieval anytime and anywhere with privacy protection. Existing PPIR suggests extracting features from cipher-images through artificially-designed methods or Convolutional Neural Networks (CNNs). Nonetheless, manual feature engineering entails additional human effort, while CNNs are sensitive to spatial permutations as they primarily manipulate local texture features. To this end, we propose an innovative end-to-end PPIR, which not only eliminates the hassle of manual features but also enables learning expressive cipher-image representations. Specifically, since Vision Transformer (ViT) exhibits excellent robustness against permutation and occlusion in images, we elaborately design an Attentive Token-Aware (ATA) ViT model and hierarchical image block encryptions, which organically complement each other in an end-to-end system. The ATA module effectively learns informative block tokens and pays less attention to trivial and noisy encrypted blocks. Besides, to deal with the problem that the generalization of the model could be hindered by data desert, we adaptively construct the cipher-image augmentations by random block swapping and block erasing, aligning with our encryption operation. Extensive experiments on two datasets validate the superior retrieval accuracy and competitive image privacy protection performance of our proposed scheme.