IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection
The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic content, but it also raises significant security concerns. Current detection methods face two major limitations: (1) the lack of multidimensional explainable datasets for generated images and videos. Existing open-source datasets (e.g., WildFake, GenVideo) rely on oversimplified binary annotations, which restrict the explainability and trustworthiness of trained detectors. (2) Prior MLLM-based forgery detectors (e.g., FakeVLM) exhibit insufficiently fine-grained interpretability in their step-by-step reasoning, which hinders reliable localization and explanation. To address these challenges, we introduce Ivy-Fake, the first large-scale multimodal benchmark for explainable AIGC detection. It consists of over 106K richly annotated training samples (images and videos) and 5,000 manually verified evaluation examples, sourced from multiple generative models and real world datasets through a carefully designed pipeline to ensure both diversity and quality. Furthermore, we propose Ivy-xDetector, a reinforcement learning model based on Group Relative Policy Optimization (GRPO), capable of producing explainable reasoning chains and achieving robust performance across multiple synthetic content detection benchmarks. Extensive experiments demonstrate the superiority of our dataset and confirm the effectiveness of our approach. Notably, our method improves performance on GenImage from 86.88% to 96.32%, surpassing prior state-of-the-art methods by a clear margin.
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Changjiang Jiang
claimed_verified
2025-06-03T08:46:29.222Z
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Changjiang Jiang
arnodjiang
user
arnodjiang
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Wenhui Dong
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Zhonghao Zhang
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Chenyang Si
claimed_verified
2025-06-10T09:29:27.754Z
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ChenyangSi
ChenyangSi
user
ChenyangSi
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Fengchang Yu
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Wei Peng
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Xinbin Yuan
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Yifei Bi
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Ming Zhao
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Zian Zhou
699ff984ebfce7fbcca91c74
Caifeng Shan
IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection
The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic content, but it also raises significant security concerns. Current detection methods face two major limitations: (1) the lack of multidimensional explainable datasets for generated images and videos. Existing open-source datasets (e.g., WildFake, GenVideo) rely on oversimplified binary annotations, which restrict the explainability and trustworthiness of trained detectors. (2) Prior MLLM-based forgery detectors (e.g., FakeVLM) exhibit insufficiently fine-grained interpretability in their step-by-step reasoning, which hinders reliable localization and explanation. To address these challenges, we introduce Ivy-Fake, the first large-scale multimodal benchmark for explainable AIGC detection. It consists of over 106K richly annotated training samples (images and videos) and 5,000 manually verified evaluation examples, sourced from multiple generative models and real world datasets through a carefully designed pipeline to ensure both diversity and quality. Furthermore, we propose Ivy-xDetector, a reinforcement learning model based on Group Relative Policy Optimization (GRPO), capable of producing explainable reasoning chains and achieving robust performance across multiple synthetic content detection benchmarks. Extensive experiments demonstrate the superiority of our dataset and confirm the effectiveness of our approach. Notably, our method improves performance on GenImage from 86.88% to 96.32%, surpassing prior state-of-the-art methods by a clear margin.
AIGC detection faces challenges with limited multidimensional explainable datasets and insufficient interpretability in existing methods, which are addressed by introducing Ivy-Fake, a large-scale multimodal benchmark, and Ivy-xDetector, a reinforcement learning model based on GRPO for explainable reasoning and improved detection performance.
Artificial Intelligence Generated Content AIGC explainable AI multimodal benchmark reinforcement learning Group Relative Policy Optimization GRPO synthetic content detection detection performance fine-grained interpretability