论文标题
相机跟踪擦除
Camera Trace Erasing
论文作者
论文摘要
相机跟踪是数字成像过程中产生的独特噪声。大多数现有法医方法分析相机跟踪以识别图像起源。在本文中，我们解决了一个新的低级视觉问题，即摄像机跟踪擦除，以揭示基于痕量的取证方法的弱点。对现有的抗飞质方法的全面研究表明，有效擦除摄像头迹线的同时避免破坏内容信号是不乏味的。为了调和这两个要求，我们提出了暹罗痕迹擦除（暹罗），其中在该siamese架构的网络培训基础上设计了一种新型的混合损失。具体而言，我们提出了嵌入的相似性，截断的保真度和交叉身份以形成混合损失。与现有的抗法透法方法相比，暹罗对于摄像机痕迹擦除具有明显的优势，这在三个代表性任务中得到了证明。代码和数据集可在https://github.com/ngchc/camerate上找到。
Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new low-level vision problem, camera trace erasing, to reveal the weakness of trace-based forensic methods. A comprehensive investigation on existing anti-forensic methods reveals that it is non-trivial to effectively erase camera trace while avoiding the destruction of content signal. To reconcile these two demands, we propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training. Specifically, we propose embedded similarity, truncated fidelity, and cross identity to form the hybrid loss. Compared with existing anti-forensic methods, SiamTE has a clear advantage for camera trace erasing, which is demonstrated in three representative tasks. Code and dataset are available at https://github.com/ngchc/CameraTE.
