Anti-Tampering Scheme for Face Images Based on Content-Aware Selective Encryption and Blockchain

With the wide application of face recognition technology, the privacy protection of face images and data authenticity guarantee face serious challenges. Traditional image encryption methods usually process the entire image, which may bring unnecessary computational overhead and lack flexibility in some scenarios. Meanwhile, the risk of image tampering is increasing. In this paper, we propose a face image tampering prevention scheme based on the combination of content-aware selective encryption and blockchain technology. The scheme first utilizes face detection techniques to locate the critical regions (ROIs) in the image, and then only these face regions containing sensitive information are encrypted with high strength, while the non-critical regions are lightly processed (e.g., blurring) to retain some of the image contextual information and reduce the amount of data processing. Critical metadata such as the hash value of the original image, the hash sequence of the critical regions, and the hash of the encryption key (or a pointer to its secure storage) will be recorded on the tamper-proof blockchain. By querying the records on the blockchain, the integrity of the image and whether the key privacy regions have been tampered with can be effectively verified. The experimental results show that this scheme can effectively detect tampering behavior while protecting the key privacy information of the face, and ensure the transparency and credibility of the verification process with the help of blockchain technology. Compared with full image encryption, this scheme can provide higher efficiency and flexibility in specific application scenarios.