From Image to Imuge: Immunized Image Generation
This addresses the need for tamper-resilient image generation for secure communication, though it appears incremental as it builds on existing generative and localization methods.
The paper tackles the problem of image tampering by introducing Imuge, a scheme that generates immunized images for self-recovery, enabling approximate recovery of original content after attacks like cropping and JPEG compression with high-quality results.
We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and fragile to diverse digital attack, i.e. image cropping and JPEG compression. To address this issue, we jointly train a U-Net backboned encoder, a tamper localization network and a decoder for image recovery. Given an original image, the encoder produces a visually indistinguishable immunized image. At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks. Several strategies are proposed to boost the training efficiency. We demonstrate that our method can recover the details of the tampered regions with a high quality despite the presence of various kinds of attacks. Comprehensive ablation studies are conducted to validate our network designs.