The Illusion of Visual Security: Reconstructing Perceptually Encrypted Images

Perceptual image encryption degrades image quality by selectively encrypting some key information of the plain images. The encrypted images are partially perceptible according to the security or quality requirements. Although several types of attacks have tried to infer privacy information from the encrypted images, they can only either extract statistical information or enhance image sketch. In this paper, we take one step further and fully recover the plain images from perceptually encrypted counterparts by designing a non-local attack network (NL-ANet). NL-ANet is composed of densely cascaded multiscale non-local modules (MSNL) and a hierarchical attention fusion module (HAFM). In particular, to better reconstruct encryption distortion, we introduce MSNL to capture powerful hierarchical features from different scales, and propose HAFM to adaptively aggregate and enhance informative hierarchical features for reconstruction. We also propose a new instantiation of the multi-head non-local block with channel attention (MHCA) to explore the long-range dependencies of global contextual information. Extensive experiments show that NL-ANet is encryption-agnostic and superior on different perceptual encryption schemes under different encryption strengths. NL-ANet also achieves better performance than state-of-the-art image restoration methods.