HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales

• URL: http://arxiv.org/abs/2607.08705v1
• Date: Thu, 09 Jul 2026 17:12:41 GMT
• Title: HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales
• Abstract summary: HumanForge is a unified, large-scale, and multiparadigm human-centric video dataset.<n>Gen2Anno coordinates six specialized agents-ranging from source profiling to MoE-based reference analysis and closed-loop forensic verification-to generate over 18K high-fidelity video segments.
• Score: 7.669728085259526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid advancements in video diffusion models and temporal editing tools have enabled the generation of highly realistic human-centric videos, posing unprecedented challenges to digital content forensics. Existing benchmarks primarily focus on either face-swapping or global text-to-video synthesis, overlooking the crucial dimensions of human-object or human-human interactions and multi-modal alignment. To address these limitations, we introduce HumanForge, a unified, large-scale, and multi-paradigm human-centric video forgery dataset. To construct and annotate this dataset without labor-intensive manual labeling or hallucinated monolithic prompts, we propose Gen2Anno, a modular active multi-agent pipeline built on LangGraph. Gen2Anno coordinates six specialized agents-ranging from source profiling to MoE-based reference analysis and closed-loop forensic verification-to generate over 18K high-fidelity video segments and produce structured, contrastive omni-annotations containing binary decisions, fine-grained artifact categories, and spatio-temporal localization. Extensive benchmarks using state-of-the-art traditional detectors and Large Multimodal Models (LMMs) demonstrate the significant challenges of zero-shot generalization and fine-grained reasoning on HumanForge. Code and dataset will be publicly released.

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