DRAW: Defending Camera-shooted RAW against Image Manipulation
Xiaoxiao Hu Qichao Ying Zhenxing Qian Sheng Li Xinpeng Zhang
RAW files are the initial measurement of scene radiance widely used in most
cameras, and the ubiquitously-used RGB images are converted from RAW data
through Image Signal Processing (ISP) pipelines. Nowadays, digital images are
risky of being nefariously manipulated. Inspired by the fact that innate
immunity is the first line of body defense, we propose DRAW, a novel scheme of
defending images against manipulation by protecting their sources, i.e.,
camera-shooted RAWs. Specifically, we design a lightweight Multi-frequency
Partial Fusion Network (MPF-Net) friendly to devices with limited computing
resources by frequency learning and partial feature fusion. It introduces
invisible watermarks as protective signal into the RAW data. The protection
capability can not only be transferred into the rendered RGB images regardless
of the applied ISP pipeline, but also is resilient to post-processing
operations such as blurring or compression. Once the image is manipulated, we
can accurately identify the forged areas with a localization network. Extensive
experiments on several famous RAW datasets, e.g., RAISE, FiveK and SIDD,
indicate the effectiveness of our method. We hope that this technique can be
used in future cameras as an option for image protection, which could
effectively restrict image manipulation at the source.