通过捕捉扩散模型的内在能量异常，实现对AI伪造图像的精准定位。
Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization
- 基于扩散过程抑制高频方差的统计特性，构建能量异常检测机制。
- 在多个AI生成伪造数据集上达到新基准，像素级定位精度显著提升。
- 适合关注AI伪造检测、数字取证的研究者与安全工程师使用。
生成式AI的发展使得图像编辑模型能够生成逼真的伪造图像，传统依赖物理噪声的方法难以应对。本文从理论上证明，扩散过程会自然抑制局部高频方差，形成可区分的统计能量间隙，区别于真实成像的熵特征。受此启发，提出FLAME框架：利用LAD图捕捉内在异常，并通过参数高效适配器集成SAM，实现像素级伪造定位。为应对检测基准滞后于生成模型演进的问题，设计EditStream自动化流水线，支持基于指令的持续训练数据合成。大量实验表明，FLAME在多个AI生成伪造数据集上达到新最佳性能，且对未见过的生成架构具有强泛化能力。代码已开源：https://github.com/phoenixnir/FLAME。
原文摘要 · Abstract (English)
Recent advancements in generative AI have led to image editing models capable of producing realistic forgeries that evade traditional image forgery localization methods, as these approaches depend on physical noise absent in synthetic data. To address this challenge, we theoretically demonstrate that the diffusion process inherently suppresses local high-frequency variance, creating a statistical energy gap that is distinguishable from the natural entropy of optical imaging. Guided by this insight, we propose FLAME, a unified framework that utilizes a LAD map to capture these intrinsic anomalies, coupled with a parameter-efficient adapter for SAM to achieve precise, pixel-level forgery localization. Furthermore, to bridge the lag between forensic benchmarks and evolving generative models, we introduce EditStream, an automated pipeline for continuous, instruction-based training data synthesis. Extensive experiments demonstrate that FLAME establishes a new state-of-the-art, significantly outperforming previous methods on AI-generated forgery datasets while effectively generalizing to unseen generative architectures. Our code is available at https://github.com/phoenixnir/FLAME.
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