DITL2: Dual-Stage Invariance Transfer Learning for Generalizable Document Image Tampering Localization

Document Image Tampering Localization (DITL) advances considerably, yet achieving robust cross-dataset generalization remains a formidable challenge for practical applications. Expanding existing document datasets for training is labor-intensive, making it appealing to incorporate data from non-document domains such as natural scene images. However, domain-specific variations, including differences in color distribution and texture, compromise the performance of joint training. To address this issue, we propose DITL2, a Dual-stage Invariance Transfer Learning framework for Document Image Tampering Localization that consists of Cross-Domain Invariance Pre-training (CDIP) and Frequency Decoupling Parameter Adaptation (FDPA). In the pre-training stage, CDIP employs style transfer and texture consistency learning to suppress domain-specific influences from tampered natural scene images, and tampering trace commonality learning to acquire domain-invariant features. In the fine-tuning stage, FDPA adapts the parameters of the pre-trained model, leveraging the general knowledge from the pre-trained model to address DITL tasks while reducing the risk of overfitting. Experiments show that this approach effectively leverages external data resources to boost model performance, achieving state-of-the-art results across a variety of cross-dataset settings.