ForensicWCT: Whitening and Coloring Transform in Forensic Latent Space for Robust Localization of AI-Edited Image Region

The spread of generative image editing tools poses a growing threat to digital safety. Developing forensic models with strong generalization is therefore critical, yet remains challenging due to two key issues: (1) semantic bias, where models rely on spurious correlations between image content and generator artifacts, and (2) the limited diversity of localization data, which restricts generalization to unseen forgery techniques. To address these challenges, we propose to reformulate forgery localization as a forensic style classification problem. We conceptualize generator-specific artifacts as a distinct ”style”, enabling the disentanglement of forensic traces from semantic content. Building on this formulation, we present ForensicWCT, a novel training framework that instantiates this concept. During training, ForensicWCT applies a whitening transform to decompose image features into semantic content and forensic style components. It then performs a style-swapping augmentation by exchanging styles across samples, while dynamically updating the corresponding ground-truth labels. This training strategy, which does not introduce additional computational cost at inference time, forces the model to learn content-agnostic artifact representations. Moreover, by elegantly injecting styles from fully-generated images into arbitrary regions, our approach enables the effective use of large-scale, diverse, fully-generated image datasets to improve localization generalization. Extensive experiments on multiple benchmarks demonstrate the effectiveness of our ForensicWCT.