AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection
Summary: arXiv:2604.16207v1 Announce Type: cross
As the landscape of digital forgeries evolves, Incremental Face Forgery Detection (IFFD) has emerged as a vital area of research. Current methodologies often depend on either data replay or simplistic binary supervision. These approaches tend to inadequately constrain the feature space, resulting in significant feature drift and catastrophic forgetting during the learning process.
To tackle these challenges, we introduce AIFIND, which stands for Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection. This innovative framework utilizes semantic anchors aimed at stabilizing the incremental learning process, thereby enhancing the detection of forged faces.
Key Innovations of AIFIND
- Artifact-Driven Semantic Prior Generator: This component is designed to instantiate invariant semantic anchors. By establishing a fixed coordinate system derived from low-level artifact cues, AIFIND creates a stable reference point for the learning process.
- Artifact-Probe Attention: This mechanism injects the semantic anchors into the image encoder, explicitly constraining dynamic visual features to align with the established stable semantic anchors. This alignment is crucial for maintaining the integrity of the feature space.
- Adaptive Decision Harmonizer: This module harmonizes the classifiers by preserving the angular relationships of semantic anchors. This preservation is essential for maintaining geometric consistency across various tasks, ensuring reliable performance in detecting face forgeries.
Experimental Validation
To demonstrate the effectiveness of AIFIND, extensive experiments were conducted across multiple incremental protocols. The results illustrate the framework’s superiority over existing methods, particularly in its ability to minimize feature drift and prevent catastrophic forgetting.
The findings suggest that AIFIND not only enhances the stability of incremental learning but also improves the overall accuracy of face forgery detection. As forgery techniques become increasingly sophisticated, the need for robust and adaptable detection systems like AIFIND is more pressing than ever.
Conclusion
AIFIND represents a significant advancement in the field of Incremental Face Forgery Detection. By leveraging artifact-aware mechanisms and semantic anchors, it addresses some of the most pressing challenges faced by existing methodologies. As research continues to evolve, frameworks like AIFIND will play a crucial role in safeguarding digital integrity in an era of pervasive forgeries.