Multi-viewpoint tampering detection for integral imaging

Current camera-array-based integral imaging lacks tampering protection, making images vulnerable to falsification and requiring high computational costs. This Letter proposes an alternative 3D integral imaging scheme that ensures clear light field display while enabling tampering detection and self-recovery. Pixel mapping and deep learning co-extract depth and angular data pixel-wisely, regulating the region of interest of 3D light field for initial verification. Multi-viewpoint recovery information is embedded to reconstruct a complete elemental image array. When tampered with, the altered region can be identified and double-recovered. Experiments demonstrate remarkable parallax effects and effective tampering detection with recovery from multiple perspectives.