Generalizing Face Forgery Detection with High-frequency Features (2103.12376v1)

Abstract: Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.

• The paper advances face forgery detection by leveraging high-frequency features to reduce method-specific bias in CNN models.
• It introduces multi-scale extraction, residual-guided spatial attention, and cross-modality modules to enhance detection accuracy.
• Experimental results demonstrate improved AUC performance across benchmarks, proving robust detection on unseen manipulation techniques.

Analyzing and Generalizing Face Forgery Detection through High-frequency Features

The paper, "Generalizing Face Forgery Detection with High-frequency Features", provides a comprehensive approach to improving the generalization capabilities of face forgery detection algorithms. The primary issue discussed is the observed limitation of existing convolutional neural network (CNN)-based detectors, which perform well on datasets synthesized by the same algorithm (within-database) but falter when exposed to forgeries generated by different methodologies (cross-database).

Core Contributions and Methodology

This research identifies and targets the bias of CNN models towards method-specific texture patterns inherent in training data synthesized by a particular forgery algorithm. Such bias restricts the model's effectiveness in recognizing forgeries crafted by alternative methods, presenting a critical challenge for generalization across diverse datasets.

To combat this, the authors propose leveraging high-frequency image features, which are less reliant on color textures and therefore represent a more method-agnostic approach to detecting discrepancies between authentic and manipulated image regions.

The authors introduce three pivotal modules to harness high-frequency features effectively:

1. Multi-scale High-frequency Feature Extraction: This module employs the use of high-pass filters from spatial rich models (SRM) to extract noise features at multiple scales. Unlike singular-image noise extraction, this approach thoroughly examines low-level features to derive a rich, informative noise profile that is less susceptible to surface-level texture variances.
2. Residual-guided Spatial Attention: This component uses spatial attention mechanisms, guided by extracted noise features, to highlight forgery traces more effectively. Implementing this attention mechanism supports the model in focusing on forgery-specific regions of an image rather than solely on superficial texture cues.
3. Cross-modality Attention Module: By facilitating interaction between the RGB domain and the newly introduced noise domain through attention mechanisms, this module enhances feature learning. It ensures reciprocal reinforcement between modalities, ultimately achieving robust representation and detection capabilities.

Evaluation and Results

The researchers conducted extensive evaluations using benchmarks such as FaceForensics++, DeepfakeDetection, DFDC, CelebDF, and DeeperForensics-1.0. They emphasize cross-database evaluation to demonstrate their method's generalization abilities, contrasting with prior works that mainly report within-database performance metrics.

The proposed model significantly outperforms baseline approaches in various cross-database tests, showcasing enhanced detection accuracy for forgeries crafted by unseen manipulation algorithms. Notably, the approach exhibited superior AUC performance when evaluated against contemporary multi-task learning and high-frequency feature-based techniques.

Implications and Future Work

The implications of this research are profound for the development of more effective face forgery detection methodologies that can be applied across diverse datasets without the need for retraining or fine-tuning. Practically, it enables the deployment of robust, scalable solutions in security-critical applications, enhancing the detection of digitally manipulated content in real-world scenarios.

Theoretically, the reconciliation of high-frequency noise features with modern deep learning architectures suggests a promising direction for other computer vision tasks where generalization poses a challenge. Future work could explore further integration with domain adaptation techniques or the extension of high-frequency features in real-time forgery detection systems.

Overall, this paper presents a technically rich and methodologically innovative contribution to the field of digital forensics, particularly in enhancing cross-database generalization for face forgery detection.