Privacy-Preserving Image Retrieval in Cloud Computing via Adaptive Secret Keys and Self-Supervised Block-Augmented Pretraining

Privacy-Preserving Image Retrieval (PPIR) enables searching for similar images on cloud servers while safeguarding image privacy. PPIR uploads encrypted images to servers to address privacy concerns and then employs deep neural networks for retrieval on extracted features from cipher-images. However, current PPIR encrypts all images with the same secret keys to maintain consistent feature spaces, lacking support for adaptive keys where distinct images are encrypted with various keys. To this end, we propose a new PPIR scheme to support adaptive keys while keeping stable feature spaces. Specifically, we design ingenious image encryption to align with feature extraction during the JPEG compression process, incorporating encryptable orthogonal transformations, shuffling, stream cipher, and sign encryption operations. Our approach extracts well-designed absolute value sequences of local blocks and global histogram features from cipher-images, ensuring stable feature spaces under adaptive keys. To enhance model generalization performance, we employ Self-Supervised Contrastive Learning (SSCL) to build a pretraining model and propose a straightforward yet efficient block-sampling augmentation technique for the structured features to drive SSCL. Moreover, our retrieval model implements a dual-attention structure to capture dependencies among local block sequences and import scores of global features. Extensive experiments on four datasets demonstrate that our approach achieves superior retrieval accuracy compared to existing schemes and maintains excellent retrieval performance under adaptive keys, effectively preserving image privacy.