Proactive image manipulation detection via deep semi-fragile watermark

Malicious image tampering refers to intentionally manipulating images to make them harmful to the owners or users. It has become one of the most severe challenges to image authenticity. Conventional methods for detecting tampering by identifying visual artifacts and distortions have limitations due to the rapid advancement of image manipulation techniques, which leave fewer detectable traces. To address these challenges, we propose a proactive media authentication method using deep learning-based semi-fragile watermarks. The designed scheme utilizes deep neural networks to embed an invisible watermark into a target image that is pixel-by-pixel entangled with it, which acts as an indicator of tampering trails. Once the watermarked image is counterfeited, the embedded watermark will exhibit changes accordingly, so we can locate the tampered regions by comparing retrieved and original watermarks. This proactive authentication mechanism makes our method effective against various image tamper techniques, including image copy&move, splicing and in-painting. Although our watermark is designed to be fragile to malicious tampering operations, it remains robust to benign image-processing operations such as JPEG compression, scaling, saturation, contrast adjustments, etc. This design enables our watermark to retain effectiveness when shared over the internet. Extensive experiments demonstrate that our method achieves state-of-the-art forgery detection with superior robustness, imperceptibility and security performance.