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.

翻译：RAW文件是大多数相机广泛使用的场景辐射初始测量值，而普遍使用的RGB图像是通过图像信号处理（ISP）流水线从RAW数据转换而来。如今，数字图像面临被恶意篡改的风险。受先天免疫系统作为身体第一道防线的启发，我们提出DRAW——一种通过保护图像源头（即相机拍摄的RAW数据）来防御图像篡改的新方案。具体而言，我们设计了一种轻量级多频部分融合网络（MPF-Net），通过频率学习与部分特征融合，使其适用于计算资源受限的设备。该网络将不可见水印作为保护信号嵌入RAW数据，其保护能力不仅可在不依赖于特定ISP流水线的情况下传递到渲染后的RGB图像中，还对模糊、压缩等后处理操作具有鲁棒性。一旦图像被篡改，我们可通过定位网络准确识别伪造区域。在RAISE、FiveK和SIDD等多个著名RAW数据集上的大量实验验证了本方法的有效性。我们希望该技术未来能作为图像保护选项集成于相机中，从源头有效限制图像篡改。