Editprint：基于自增强训练编辑指纹的通用数字图像取证
Editprint: General Digital Image Forensics via Editing Fingerprint with Self-Augmentation Training
把「成像/编辑/传输处理链」当作可自增强构造的监督信号，用文本描述编辑链做语言引导对比学习，只需极少无标注数据即可学到跨相机、跨编辑的通用取证表征；做图像溯源、合成图检测或数据来源可信度研究时，可直接复用其在线编辑池与对比监督范式。
原文依据 we devise an online editing pool based on self-augmentation strategies
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论数字图像取证可在相机来源识别（CSI）、合成图像检测（SID）和社交网络溯源（SNP）等任务中确保信息可信度。这些任务通常依赖相机内处理、拍摄后编辑或合成生成所留下的图像处理历史线索。然而，现有大多数取证方法存在明显局限：1）它们往往只关注相机特有的痕迹（例如著名的 PRNU）；2）它们需要大量带标注的训练数据。为克服这些限制，我们提出 Editprint，一种新颖的通用取证特征，仅需极少的无标注训练数据即可捕获高度多样的相机内与相机外处理历史线索。理想情况下，我们希望任何经历相同成像、编辑与传输过程的图像都会产生相同的 Editprint，反之亦然。为对相机内与相机外操作建模，我们设计了一个基于自增强策略的在线编辑池。该编辑池仅需极少（例如 10 个）训练数据，即可模拟由相机内处理及后续相机外操作所产生的大量（例如 10^7 条）编辑链与痕迹。为确保 Editprint 在不同编辑链之间具有高判别能力，我们提出以这些编辑链的文本描述作为标签，并通过语言引导的对比学习对其 Editprint 进行监督。大量实验表明，Editprint 优于现有的自监督取证方法，在 SNP 和 SID 等非相机应用中尤为突出。我们希望 Editprint 能启发取证社区，并成为自监督取证的一个新基准。
英文原文摘要 1714 字符
Digital image forensics can ensure information credibility in tasks like camera source identification (CSI), synthetic image detection (SID), and social network provenance (SNP). These tasks typically rely on image processing history clues left by in-camera operations, post-capture editing, or synthetic generation. However, most existing forensic methods have obvious limitations: 1) they often only focus on camera-specific traces (e.g., the well-known PRNU), and 2) they demand a substantial amount of annotated training data. To address these constraints, we propose Editprint, a novel general forensic feature that captures highly diverse in- and out-camera processing history clues with minimal unlabeled training data. Ideally, we expect that any images undergoing the same imaging, editing, and transmission processes would yield identical Editprints, and vice versa. To model the in- and out-camera operations, we devise an online editing pool based on self-augmentation strategies. Requiring only minimal (e.g., 10) training data, the editing pool can simulate massive (e.g., 10^\text 7 ) editing chains and traces arising from the in-camera processing and the subsequent out-camera operations. To ensure that Editprint exhibits high discriminative capabilities across various editing chains, we propose using textual descriptions of these chains as labels and supervising their Editprints through language-guided contrastive learning. Extensive experiments show Editprint outperforms existing self-supervised forensics, particularly in non-camera applications such as SNP and SID. We hope that Editprint would inspire the forensic community and serve as a novel benchmark for self-supervised forensics.