Self-Healing IMages: Unifying Manipulation Detection and Recovery via Latent Self-Embedding

Generative image editing raises two questions: where was an image manipulated, and what content was overwritten? Passive localization addresses the first but cannot recover source content that is no longer present. Existing self-recovery methods preserve this content through embedded representations, often coupling payload construction and spatial dispersion at image resolution. We introduce SHIM (Self-Healing IMages), which embeds a compact, spatially shuffled source latent to support both localization and recovery. The receiver decodes the extracted latent in two orders: direct decoding retains manipulation-related inconsistencies, while inverse-shuffle decoding restores source alignment. A locator combines both views with the received image, and a mask-guided refiner corrects the aligned estimate within the predicted repair support. On valAGE-Set, SHIM maintains high container fidelity while achieving accurate manipulation localization and effective source-content recovery after channel degradation. A separate evaluation on COCO, ImageNet, and CelebA-HQ shows stronger dataset-averaged recovery than Imuge+ and ReImage across diverse channel conditions. Controlled analyses show that the direct view complements the aligned view and that learned refinement improves recovery over raw composition. These results support compact latent self-embedding as a common representation for proactive localization and source-conditioned recovery.