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.

翻译：为了解决这个问题,我们联合培训了一个U-Net的骨干编码器、一个篡改本地化网络和一个图像恢复解码器。根据一个原始图像,编码器生成了一个视觉无法分辨的免疫图像。在接受者一方,核实网络将恶意修改的地方化,而原始内容可以由解码器大致恢复,尽管袭击已经存在。提出了提高培训效率的若干战略。我们证明,尽管存在各种袭击,我们的方法可以恢复被篡改区域高质量的细节。进行了全面的烧蚀研究,以验证我们的网络设计。