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Towards Explainable Partial-AIGC Image Quality Assessment

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arxiv 2504.09291 v1 pith:V4DEOXT5 submitted 2025-04-12 cs.CV cs.MM

Jiaying Qian , Ziheng Jia , Zicheng Zhang , Zeyu Zhang , Guangtao Zhai , Xiongkuo Min This is my paper
classification cs.CVcs.MM
keywords qualityassessmentimagesimageexplainablelocalizedpartial-aigcai-driven
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid advancement of AI-driven visual generation technologies has catalyzed significant breakthroughs in image manipulation, particularly in achieving photorealistic localized editing effects on natural scene images (NSIs). Despite extensive research on image quality assessment (IQA) for AI-generated images (AGIs), most studies focus on fully AI-generated outputs (e.g., text-to-image generation), leaving the quality assessment of partial-AIGC images (PAIs)-images with localized AI-driven edits an almost unprecedented field. Motivated by this gap, we construct the first large-scale PAI dataset towards explainable partial-AIGC image quality assessment (EPAIQA), the EPAIQA-15K, which includes 15K images with localized AI manipulation in different regions and over 300K multi-dimensional human ratings. Based on this, we leverage large multi-modal models (LMMs) and propose a three-stage model training paradigm. This paradigm progressively trains the LMM for editing region grounding, quantitative quality scoring, and quality explanation. Finally, we develop the EPAIQA series models, which possess explainable quality feedback capabilities. Our work represents a pioneering effort in the perceptual IQA field for comprehensive PAI quality assessment.

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  1. DFBench: Benchmarking Deepfake Image Detection Capability of Large Multimodal Models

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    DFBench adds a 540,000-image benchmark with 12 modern generators, partial edits, and distorted real images, and its three-model LMM ensemble, MoA-DF, reaches near-perfect recall on its own test split.

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