Visual Content Revealing From Perceptually Encrypted Images

Perceptual image encryption serves as a pivotal mechanism for delegating processing while ensuring the visual security of image data. The robustness of such encryption schemes is traditionally evaluated through cryptanalysis techniques, yet these approaches heavily rely on manual labor and prerequisite knowledge of the encryption algorithms. Recently, some works attempt to reveal visual content from perceptually encrypted images Based on CNN architectures. However, it is still tricky for these increasingly complex methods to reveal informative visual details. In this study, we focus on the extraction and utilization of inherent hierarchical features within the input image itself to significantly advances the field. To achieve it, we present a novel Progressive Fusion Attack Network (PFAN) to fully explore the hierarchical features. PFAN incorporates multiple subbranches, forming a progressive fusion structure that facilitates informative hierarchical feature representations and offers robust model fault tolerance. To enhance the reconstruction of encryption-induced distortions, we incorporate a Multiscale Feature Extraction Module (MFEM) that captures robust hierarchical features across various scales. Meanwhile, a Hierarchical Feature Fusion Module (HFFM) is designed to adaptively integrate and highlight the optimal feature representations, further optimizing the visual content reconstruction process. Extensive experimental evaluation demonstrates that PFAN exhibits remarkable agnosticism towards different perceptual encryption schemes and encryption strengths, achieving superior performance. Furthermore, PFAN outperforms state-of-the-art CNN-based image restoration methods in terms of effectiveness and generalizability.