arXiv cs.AI· Riccardo Ziglio, Eleonora Marchesini, Andrea Montibeller, Giulia Boato, Silvio Ranise, Cecilia Pasquini·· 3 小时前AI 评分37
基于部分 C2PA 溯源信息的图像取证增强方法
Enhanced image forensics through partial C2PA-based provenance information
AI 导读
一项被 IEEE WIFS 2026 接收的研究提出利用部分 Content Credentials（部分 C2PA 溯源信息）增强图像取证的方法：先通过对部分 CC 的结构分析重建最后签名者施加的编辑操作，再将重建结果用于适配取证检测器，缓解分布偏移。
正文 · 原文
Abstract:Cryptographically verifiable C2PA-based Content Credentials (CCs) can be attached to freshly acquired or generated digital images but also to already existing ones with no previous provenance information, for instance after processing through trusted C2PA-enabled software tools. The latter case leads to images with so-called partial CCs, which capture only the last operations applied, while prior provenance remains unknown and can be investigated through signal forensics. This paper addresses the forensic analysis of such images, highlighting the opportunity to leverage the partial CCs as additional trusted knowledge to adapt forensic detectors towards the statistical characteristics of the image under analysis and mitigate potential distribution shifts. To this end, a two-step strategy is envisioned: (i) reconstructing the operations applied by the last signer through a structural analysis of the partial CCs; (ii) exploiting the obtained reconstruction to enhance the forensic detector of the analyst through an adaptation process. An empirical analysis on partial CCs produced by commercial C2PA-enabled software tools through different editing operations is carried out, providing evidence on the feasibility of the reconstruction in practical cases. An experimental case study on the forensic task of synthetic image detection is then presented, showing that the proposed strategy can significantly enhance the performance of state-of-the-art detectors on test images that are postprocessed via C2PA-enabled software tools after being generated/acquired.
| Comments: | paper accepted to IEEE International Workshop on Information Forensics and Security (WIFS) 2026 |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multimedia (cs.MM); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2610.38639 [cs.CR] |
| (or arXiv:2610.38639v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38639 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Riccardo Ziglio Mr. [view email]
[v1]
Wed, 28 Oct 2026 10:53:05 UTC (3,241 KB)
来源：arXiv cs.AI · arxiv.org