DRAW: Defending Camera-shooted RAW against Image Manipulation

TL;DR

This paper introduces DRAW, a method that embeds invisible watermarks into RAW images to protect against manipulation, enabling source-based detection and localization of forgeries even after post-processing.

Contribution

The paper proposes a lightweight, device-friendly watermarking scheme for RAW images that resists manipulation and post-processing, with a dedicated localization network for forgery detection.

Findings

Effective in identifying manipulated regions in RAW images

Resilient to post-processing like blurring and compression

Works across multiple RAW datasets such as RAISE, FiveK, and SIDD

Abstract

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.