The proposed Face Forgery Detection (FFD) workflow can be effectively extended to other visual tasks, such as general image manipulation detection and multimodal forgery detection, by leveraging the foundational principles of robust backbone pre-training, fine-tuning, and inference optimization.
General Image Manipulation Detection: The FFD workflow emphasizes the importance of pre-training backbones on datasets that closely resemble the target domain. For general image manipulation detection, backbones can be pre-trained on diverse datasets that include various forms of image manipulations, such as photo editing, compositing, and retouching. By employing self-supervised learning techniques tailored to capture artifacts and inconsistencies typical of manipulated images, the backbone can develop a nuanced understanding of subtle visual cues indicative of manipulation. The fine-tuning phase can then focus on specific manipulation types, enhancing the model's ability to generalize across different manipulation techniques.
Multimodal Forgery Detection: Extending the FFD workflow to multimodal forgery detection involves integrating information from multiple modalities, such as images, videos, and audio. The backbone can be pre-trained on multimodal datasets that encompass various forgery types across these modalities. Techniques such as cross-modal learning can be employed during fine-tuning to ensure that the model learns to identify inconsistencies and artifacts that may arise when different modalities are manipulated together. Additionally, the threshold optimization mechanism can be adapted to account for the confidence levels across modalities, improving the reliability of the final predictions.
By applying the principles of robust backbone development and fine-tuning strategies from the FFD workflow, other visual tasks can benefit from enhanced feature representation capabilities and improved generalization, ultimately leading to more effective detection of various forms of visual forgery.