When an Image Cipher Meets Computer Vision: A Survey on Semantic‐Aware Selective Encryption

ABSTRACT With the explosive growth in the volume of image usage, selective image encryption (SIE) has emerged as an efficient method to enhance encryption efficiency. The challenge of how to identify images containing sensitive content has long been a difficult issue. Deep learning technology, with its powerful semantic extraction capabilities, has naturally become an auxiliary tool for recognizing images containing specific content. This review primarily focuses on recent advancements in SIE integrated with semantic understanding. First, it reviews the current state of development in image ROI encryption. Subsequently, it proposes two semantic‐aware SIE schemes based on image‐to‐image and text‐to‐image search paradigms. The review also introduces evaluation metrics for assessing both the encryption algorithms and deep learning models involved in such SIE systems. Finally, it analyzes potential security issues in SIE, such as privacy protection of deep learning models and leakage of ROI edge regions, as well as possible optimization directions, including model lightweighting and encryption parallelization to enhance efficiency. In conclusion, this review indicates that selective encryption is not limited to ROI‐based approaches but also includes semantic retrieval followed by targeted encryption. Moreover, with the integration of deep learning models, considerations regarding security and efficiency have become more complex, representing key areas for further exploration in future research.