简单即正义：视觉基础模型中通用可推广AIGI检测的出现
Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models
AI总结：
本文提出通过简单线性分类器在现代视觉基础模型冻结特征上训练，实现超越专门检测器的AIGI检测性能，尤其在真实世界数据集上表现更优。
摘要：
尽管专门用于检测AI生成图像（AIGI）的检测器在经过整理的基准上能实现接近完美的准确性，但在真实、野外场景中却表现出急剧的性能下降。在本工作中，我们证明简单性胜过复杂的架构设计。一个在现代视觉基础模型冻结特征上训练的简单线性分类器，建立了新的最先进的状态。通过全面评估，涵盖传统基准、未见过的生成器和具有挑战性的野外分布，我们展示了该基线不仅在标准基准上与专门检测器相匹配，而且在野外数据集上明显优于它们，准确率提升超过30%。我们认为这种优越能力是由于大规模预训练数据中包含合成内容所驱动的涌现属性。我们追溯这种能力的来源，发现两种不同的数据暴露表现：视觉-语言模型内化了显式的伪造语义概念，而自监督学习模型则从预训练数据中隐式地获得了辨别性刑侦特征。然而，我们还揭示了持续的局限性：这些模型在重新捕获和传输时会表现出性能下降，对VAE重建和局部编辑仍然盲。我们最后倡导在AI刑侦领域进行范式转变，从过度拟合静态基准到利用基础模型的不断演化的世界知识以实现现实世界的可靠性。
英文摘要：
While specialized detectors for AI-Generated Images (AIGI) achieve near-perfect accuracy on curated benchmarks, they suffer from a dramatic performance collapse in realistic, in-the-wild scenarios. In this work, we demonstrate that simplicity prevails over complex architectural designs. A simple linear classifier trained on the frozen features of modern Vision Foundation Models , including Perception Encoder, MetaCLIP 2, and DINOv3, establishes a new state-of-the-art. Through a comprehensive evaluation spanning traditional benchmarks, unseen generators, and challenging in-the-wild distributions, we show that this baseline not only matches specialized detectors on standard benchmarks but also decisively outperforms them on in-the-wild datasets, boosting accuracy by striking margins of over 30\%. We posit that this superior capability is an emergent property driven by the massive scale of pre-training data containing synthetic content. We trace the source of this capability to two distinct manifestations of data exposure: Vision-Language Models internalize an explicit semantic concept of forgery, while Self-Supervised Learning models implicitly acquire discriminative forensic features from the pretraining data. However, we also reveal persistent limitations: these models suffer from performance degradation under recapture and transmission, remain blind to VAE reconstruction and localized editing. We conclude by advocating for a paradigm shift in AI forensics, moving from overfitting on static benchmarks to harnessing the evolving world knowledge of foundation models for real-world reliability.
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