Direction-aware Cross-modal Transformer for Image Tampering Localization

Unlike conventional fusion strategies that simply concatenate or sum multi-modal features, our framework explicitly incorporates directional awareness to model structural consistency of tampered regions across different orientations. This design enables the model to better capture boundary characteristics and fine-grained geometric patterns that are often weakened or distorted by post-processing operations.Specifically, the proposed approach consists of three key components.

First, a dual-branch feature extraction architecture is adopted to separately learn semantic information from the RGB domain and forensic cues from the noise domain. This design allows the model to preserve both high-level semantic context and low-level manipulation traces. Second, a direction-aware mechanism is introduced to encode orientation-sensitive information, guiding the network to emphasize directional consistency within tampered regions while suppressing irrelevant background responses.

By explicitly modeling directional dependencies, the proposed method enhances the discriminative representation of tampered boundaries and internal structures. Third, a cross-modal reasoning module is developed to facilitate adaptive interaction between RGB and noise features. This module enables mutual guidance and information refinement across modalities, thereby improving feature complementarity and reducing redundancy during the localization process.

Extensive experiments are conducted on multiple publicly available image tampering localization benchmarks to evaluate the effectiveness of the proposed method. Quantitative results demonstrate that the proposed approach consistently outperforms several state-of-the-art methods in terms of commonly used evaluation metrics, including F1-score and Intersection-over-Union (IoU). In particular, the proposed method shows notable performance gains in challenging scenarios involving complex tampering types and multiple post-processing operations, such as compression, blurring, and rescaling.

Qualitative comparisons further illustrate that the proposed framework produces more accurate and coherent localization results, especially along tampered boundaries and fine structural details. In addition, robustness experiments indicate that the proposed direction-aware cross-modal reasoning framework maintains stable performance under various degradations commonly encountered in real-world image dissemination environments.

This robustness can be attributed to the effective integration of directional structural cues and complementary cross-modal information, which jointly enhance the model’s ability to distinguish manipulated regions from authentic content. In conclusion, this work presents a novel direction-aware cross-modal reasoning approach for image tampering localization. By explicitly modeling directional consistency and leveraging cross-modal feature interactions, the proposed method achieves improved localization accuracy and robustness in complex manipulation scenarios.

The experimental results demonstrate that the proposed framework provides a reliable and effective solution for image tampering localization, offering promising potential for practical applications in digital image forensics and multimedia security.