ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization

• URL: http://arxiv.org/abs/2505.11003v1
• Date: Fri, 16 May 2025 08:49:59 GMT
• Title: ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
• Authors: Bo Du, Xuekang Zhu, Xiaochen Ma, Chenfan Qu, Kaiwen Feng, Zhe Yang, Chi-Man Pun, Jian Liu, Jizhe Zhou,
• Abstract summary: ForensicHub is the first unified benchmark for all-domain fake image detection and localization.<n>It decomposes forensic pipelines into interchangeable components across datasets, transforms, models, and evaluators.<n>It offers 8 key actionable insights into FIDL model architecture, dataset characteristics, and evaluation standards.
• Score: 48.147576833781386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of Fake Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-generated image detection (AIGC), and document image manipulation localization (Doc). Although individual benchmarks exist in some domains, a unified benchmark for all domains in FIDL remains blank. The absence of a unified benchmark results in significant domain silos, where each domain independently constructs its datasets, models, and evaluation protocols without interoperability, preventing cross-domain comparisons and hindering the development of the entire FIDL field. To close the domain silo barrier, we propose ForensicHub, the first unified benchmark & codebase for all-domain fake image detection and localization. Considering drastic variations on dataset, model, and evaluation configurations across all domains, as well as the scarcity of open-sourced baseline models and the lack of individual benchmarks in some domains, ForensicHub: i) proposes a modular and configuration-driven architecture that decomposes forensic pipelines into interchangeable components across datasets, transforms, models, and evaluators, allowing flexible composition across all domains; ii) fully implements 10 baseline models, 6 backbones, 2 new benchmarks for AIGC and Doc, and integrates 2 existing benchmarks of DeepfakeBench and IMDLBenCo through an adapter-based design; iii) conducts indepth analysis based on the ForensicHub, offering 8 key actionable insights into FIDL model architecture, dataset characteristics, and evaluation standards. ForensicHub represents a significant leap forward in breaking the domain silos in the FIDL field and inspiring future breakthroughs.

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