On the Holistic Approach for Detecting Human Image Forgery

• URL: http://arxiv.org/abs/2601.04715v1
• Date: Thu, 08 Jan 2026 08:33:22 GMT
• Title: On the Holistic Approach for Detecting Human Image Forgery
• Authors: Xiao Guo, Jie Zhu, Anil Jain, Xiaoming Liu,
• Abstract summary: We introduce HuForDet, a holistic framework for human image forgery detection.<n>A contextualized forgery detection branch leverages a Multi-Modal Large Language Model (MLLM) to analyze full-body semantic consistency.<n>Our HuForDet achieves state-of-the-art forgery detection performance and superior robustness across diverse human image forgeries.
• Score: 20.765860380888057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of AI-generated content (AIGC) has escalated the threat of deepfakes, from facial manipulations to the synthesis of entire photorealistic human bodies. However, existing detection methods remain fragmented, specializing either in facial-region forgeries or full-body synthetic images, and consequently fail to generalize across the full spectrum of human image manipulations. We introduce HuForDet, a holistic framework for human image forgery detection, which features a dual-branch architecture comprising: (1) a face forgery detection branch that employs heterogeneous experts operating in both RGB and frequency domains, including an adaptive Laplacian-of-Gaussian (LoG) module designed to capture artifacts ranging from fine-grained blending boundaries to coarse-scale texture irregularities; and (2) a contextualized forgery detection branch that leverages a Multi-Modal Large Language Model (MLLM) to analyze full-body semantic consistency, enhanced with a confidence estimation mechanism that dynamically weights its contribution during feature fusion. We curate a human image forgery (HuFor) dataset that unifies existing face forgery data with a new corpus of full-body synthetic humans. Extensive experiments show that our HuForDet achieves state-of-the-art forgery detection performance and superior robustness across diverse human image forgeries.

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