DRAW：防御相机拍摄的RAW图像免受图像篡改
DRAW: Defending Camera-shooted RAW against Image Manipulation
AI总结：
提出DRAW方案，通过轻量级MPF-Net将不可见水印嵌入相机拍摄的RAW数据，使渲染后的RGB图像对篡改具有鲁棒性并能准确定位伪造区域，在源头限制图像篡改。
摘要：
RAW文件是大多数相机广泛使用的场景辐射度的初始测量值，而普遍使用的RGB图像是通过图像信号处理（ISP）流水线从RAW数据转换而来的。如今，数字图像面临被恶意篡改的风险。受先天免疫是人体第一道防线这一事实的启发，我们提出了DRAW，这是一种通过保护图像来源（即相机拍摄的RAW）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的多频率部分融合网络（MPF-Net），通过频率学习和部分特征融合，对计算资源有限的设备友好。它将不可见水印作为保护信号引入RAW数据中。无论应用何种ISP流水线，这种保护能力不仅能转移到渲染后的RGB图像中，而且对模糊或压缩等后处理操作具有鲁棒性。一旦图像被篡改，我们可以通过定位网络准确识别伪造区域。在RAISE、FiveK和SIDD等著名RAW数据集上的大量实验表明了我们方法的有效性。我们希望这项技术能作为图像保护选项用于未来的相机中，从而在源头有效限制图像篡改。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
