Abstract
The proliferation of highly realistic AI-generated images poses critical challenges for digital forensics, demanding precise pixel-level localization of manipulated regions. Existing methods predominantly learn discriminative patterns of specific forgeries and often struggle with novel manipulations as editing techniques continue to evolve. We propose the Iterative Forgery Amplifier Network (IFA-Net), which shifts from learning "what is fake" to modeling "what is real". Grounded in the principle that all manipulations deviate from the natural image manifold, IFA-Net leverages a frozen Masked Autoencoder (MAE) pretrained on real images as a universal realness prior. Our framework operates through a two-stage closed-loop process: an initial Dual-Stream Segmentation Network (DSSN) fuses the original image with MAE reconstruction residuals for coarse localization, followed by a Task-Adaptive Prior Injection (TAPI) module that converts this coarse prediction into guiding prompts to steer the MAE decoder and amplify reconstruction failures in suspicious regions for precise refinement. Extensive experiments on four diffusion-based inpainting benchmarks show that IFA-Net achieves an average improvement of 6.5% in IoU and 8.1% in F1-score over the second-best method, while demonstrating strong generalization to traditional manipulation types.
Abstract (translated)
高度逼真的AI生成图像的泛滥对数字取证提出了严峻挑战，需要精确地定位被篡改区域。现有的方法主要侧重于学习特定伪造品的鉴别模式，并且往往难以应对不断演进的编辑技术所带来的新类型操纵。我们提出了一种迭代伪造放大网络（Iterative Forgery Amplifier Network, IFA-Net），该网络从“识别假图像”转向了“建模真实图像”。基于所有篡改都会偏离自然图像流形的原则，IFA-Net利用一个预训练于真实图像的冻结Masked Autoencoder (MAE) 作为普遍的真实度先验。 我们的框架通过一个两阶段闭合循环过程运行：首先是一个初始的双通道分割网络（Dual-Stream Segmentation Network, DSSN），它将原始图像与MAE重建残差融合，以进行粗略定位；随后是一个任务适应性先验注入（Task-Adaptive Prior Injection, TAPI）模块，该模块将初步预测转化为指导提示，引导MAE解码器并在可疑区域放大重建失败，从而实现精确的细化。 在四个基于扩散的方法填充基准数据集上的广泛实验表明，IFA-Net相较于次佳方法平均提升了6.5%的交并比（IoU）和8.1%的F1分数，并展示了对传统篡改类型的强大泛化能力。
URL
https://arxiv.org/abs/2602.18842
