DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2307.01426v2)

Abstract: A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://github.com/SCLBD/DeepfakeBench.

• The paper introduces DeepfakeBench, a unified benchmark that standardizes data preprocessing and evaluation protocols for deepfake detection.
• It integrates 15 state-of-the-art detection algorithms across varied modalities, facilitating fair, cross-domain comparative studies.
• Experimental results show that advanced architectures like Xception outperform simpler models, highlighting the critical role of robust backbones.

Overview of DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

Deepfake technology, with its ability to manipulate facial imagery seamlessly, has gained significant attention in various sectors. However, the unchecked proliferation of such technology poses serious risks, including erosion of trust and privacy violations. The paper "DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection" addresses critical challenges in the field of deepfake detection, notably the lack of a standardized, unified benchmark, which hinders fair performance comparison and could lead to misleading results.

Key Contributions

The authors present DeepfakeBench, a comprehensive benchmark designed to address inconsistencies in data processing, experimental settings, and evaluation metrics. The benchmark offers three primary contributions:

1. Unified Data Management System: By establishing a consistent data input protocol for all detectors, DeepfakeBench ensures uniformity in preprocessing steps, ranging from face detection to mask extraction. This alleviates time-consuming efforts in data preparation and provides a user-friendly API for researchers.
2. Integrated Framework for Detection Methods: The benchmark hosts 15 state-of-the-art detection algorithms, categorized into naive, spatial, and frequency detectors, each implemented in a modular codebase. This allows for direct comparisons under standardized training and evaluation settings.
3. Standardized Evaluation Protocols: Employing multiple evaluation metrics such as AUC, AP, and EER, DeepfakeBench promotes transparency and reproducibility across results, while offering extensive analysis tools such as t-SNE and Grad-CAM for deeper insights into detection performance.

Evaluation and Analysis

Within-Domain and Cross-Domain Evaluations: Experiments reveal that while naive detectors like Xception and EfficientNetB4 perform comparably well to more advanced algorithms, the presence of pre-training and data augmentation significantly enhances detection performance. Moreover, cross-manipulation evaluations indicate a notable generalization gap between training on specific forgeries and evaluating on unseen ones, underscoring the importance of capturing generic forgery artifacts.

Feature Exploration: The paper highlights that choice of backbone architecture substantially influences model performance. Architectures like Xception and EfficientNetB4 consistently outperform simpler models such as ResNet34, illustrating the architectural advantage in deepfake detection.

Implications and Future Directions

The establishment of DeepfakeBench represents a critical step towards standardizing the evaluation of deepfake detection algorithms. By providing a comprehensive, modular framework and extensive datasets, it encourages innovation and rigorous testing of new methodologies. However, future developments should consider incorporating video-level detectors and extending evaluations to images generated by GANs and diffusion methods.

Societal Impact: While DeepfakeBench enhances detection capabilities, it also highlights ethical considerations. Though the benchmark is intended for constructive research, it could inadvertently aid in evasion tactics. Ensuring responsible access and continuously evolving the benchmark to address emerging threats is vital.

Conclusion

DeepfakeBench serves as a pivotal resource for researchers, offering a standardized, comprehensive platform that facilitates fair comparison, deep insight, and innovative exploration of deepfake detection techniques. By addressing existing challenges, it lays the groundwork for future advancements and collaborative research efforts in combating malicious deepfake activity.