ABSTRACT With the exponential expansion of image data, data privacy security is facing significant challenges. The privacy protection methods that are currently in use generally suffer from inefficiency and absence of semantic understanding, resulting in either excessive encryption or insufficient protection, making it difficult to comply with privacy protection requirements in complex scenarios. To this end, this paper proposes the Encrypt Anything Model (EAM), which is capable of performing fine‐grained hierarchical and region‐based encryption on the content within images. EAM constructs the perception unit by integrating vision foundation models, leverages cross‐modal feature fusion techniques to accurately identify and segment privacy‐related entities in images and applies hierarchical and region‐specific encryption to different areas according to the privacy level of each entity. During the decryption phase, EAM introduces a differentiated decryption mechanism based on a permission matrix, which controls the image content that users are allowed to recover through dynamic token allocation, thereby enabling multilevel privacy protection. Experimental results across multiple privacy scenarios validate the superior performance of EAM in terms of detection accuracy and privacy entity coverage. Qualitative analysis further demonstrates that the model can effectively obscure sensitive information while maximising the usability of nonsensitive regions. By constructing a complete pipeline of ‘perceptual recognition, hierarchical encryption and differentiated decryption’, this study achieves fine‐grained governance of image privacy, providing a flexible and extensible general framework to meet multilevel privacy protection requirements and image privacy governance needs in open scenarios.
Han et al. (Thu,) studied this question.