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.

翻译：人工智能生成内容（AIGC）技术的快速发展使得高质量合成内容的生成成为可能，但也引发了重大安全隐忧。当前检测方法面临两大局限：（1）缺乏针对生成图像与视频的多维可解释数据集。现有开源数据集（如WildFake、GenVideo）依赖过度简化的二元标注，限制了训练所得检测器的可解释性与可信度。（2）基于多模态大语言模型（MLLM）的先前伪造检测器（如FakeVLM）在逐步推理过程中展现出不够细粒度的可解释性，阻碍了可靠的定位与解释。针对这些挑战，我们提出Ivy-Fake——首个用于可解释AIGC检测的大规模多模态基准。该基准包含超过10.6万条丰富标注的训练样本（图像与视频）及5000个人工验证的评估样本，这些样本通过精心设计的流程从多个生成模型及真实世界数据集中采集，确保了多样性与质量。此外，我们提出Ivy-xDetector——一种基于分组相对策略优化（GRPO）的强化学习模型，能够生成可解释的推理链，并在多个合成内容检测基准上实现稳健性能。大量实验证明了我们数据集的优越性，并验证了所提方法的有效性。值得注意的是，我们的方法将GenImage上的性能从86.88%提升至96.32%，以显著优势超越了此前最先进的方法。