The rapid and unrestrained advancement of generative artificial intelligence (AI) presents a double-edged sword: while enabling unprecedented creativity, it also facilitates the generation of highly convincing deceptive content, undermining societal trust. As image generation techniques become increasingly sophisticated, detecting synthetic images is no longer just a binary task: it necessitates interpretable, context-aware methodologies that enhance trustworthiness and transparency. However, existing detection models primarily focus on classification, offering limited explanatory insights into image authenticity. In this work, we propose FakeScope, an expert multimodal model (LMM) tailored for AI-generated image forensics, which not only identifies AI-synthetic images with high accuracy but also provides rich, interpretable, and query-driven forensic insights. We first construct FakeChain dataset that contains linguistic authenticity reasoning based on visual trace evidence, developed through a novel human-machine collaborative framework. Building upon it, we further present FakeInstruct, the largest multimodal instruction tuning dataset containing 2 million visual instructions tailored to enhance forensic awareness in LMMs. FakeScope achieves state-of-the-art performance in both closed-ended and open-ended forensic scenarios. It can distinguish synthetic images with high accuracy while offering coherent and insightful explanations, free-form discussions on fine-grained forgery attributes, and actionable enhancement strategies. Notably, despite being trained exclusively on qualitative hard labels, FakeScope demonstrates remarkable zero-shot quantitative capability on detection, enabled by our proposed token-based probability estimation strategy. Furthermore, FakeScope exhibits strong generalization and in-the-wild ability, ensuring its applicability in real-world scenarios.

翻译：生成式人工智能的快速且不受约束的发展是一把双刃剑：它在实现前所未有的创造力的同时，也催生了高度逼真的欺骗性内容，从而损害了社会信任。随着图像生成技术日益精进，检测合成图像不再仅仅是一项二元分类任务，它需要可解释、上下文感知的方法来增强可信度和透明度。然而，现有检测模型主要侧重于分类，对图像真实性的解释性洞察有限。在本研究中，我们提出了FakeScope，一个专为AI生成图像取证设计的专家多模态模型，它不仅能够高精度识别AI合成图像，还能提供丰富、可解释、查询驱动的取证分析。我们首先构建了FakeChain数据集，该数据集包含基于视觉痕迹证据的语言真实性推理，通过一种新颖的人机协作框架开发而成。在此基础上，我们进一步提出了FakeInstruct，这是最大的多模态指令调优数据集，包含200万条专门用于增强LMM取证意识的视觉指令。FakeScope在封闭式和开放式取证场景中均实现了最先进的性能。它能够高精度区分合成图像，同时提供连贯且深入的推理、对细粒度伪造属性的自由讨论以及可操作的增强策略。值得注意的是，尽管仅使用定性硬标签进行训练，FakeScope通过我们提出的基于标记的概率估计策略，在检测任务上展现了卓越的零样本量化能力。此外，FakeScope表现出强大的泛化能力和实际应用能力，确保了其在现实场景中的适用性。