DeepFakes: a New Threat to Face Recognition? Assessment and Detection (1812.08685v1)

Abstract: It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto pornographic videos, call for automated ways to detect these Deepfake videos. To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. We used open source software based on GANs to create the Deepfakes, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. To demonstrate this impact, we generated videos with low and high visual quality (320 videos each) using differently tuned parameter sets. We showed that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to Deepfake videos, with 85.62% and 95.00% false acceptance rates respectively, which means methods for detecting Deepfake videos are necessary. By considering several baseline approaches, we found that audio-visual approach based on lip-sync inconsistency detection was not able to distinguish Deepfake videos. The best performing method, which is based on visual quality metrics and is often used in presentation attack detection domain, resulted in 8.97% equal error rate on high quality Deepfakes. Our experiments demonstrate that GAN-generated Deepfake videos are challenging for both face recognition systems and existing detection methods, and the further development of face swapping technology will make it even more so.

Analysis of DeepFakes and their Implications for Face Recognition and Detection

The paper "DeepFakes: a New Threat to Face Recognition? Assessment and Detection" by Pavel Korshunov and Sébastien Marcel provides an in-depth examination of the vulnerabilities that state-of-the-art face recognition systems face when confronted with Deepfake technology. This work introduces a publicly available dataset of Deepfake videos and discusses their implications on facial recognition systems and detection methodologies.

Key Contributions

The authors provide several notable contributions:

1. Dataset Release: The paper introduces the first publicly available dataset of Deepfake videos, leveraging GAN-based techniques to create face-swapped content from the VidTIMIT database. This dataset comprises both low and high-quality video variations, serving as a crucial resource for research into Deepfake detection and analysis.
2. Vulnerability Analysis: The paper evaluates the susceptibility of advanced face recognition systems, specifically VGG and Facenet architectures, to Deepfake videos. The results demonstrate significant vulnerability, with false acceptance rates reaching 85.62% for VGG and 95.00% for Facenet on high-quality Deepfakes.
3. Detection Methodologies: Several baseline detection methods are assessed for their effectiveness. These include audio-visual lip-sync inconsistency detection and image quality metrics with machine learning classifiers. The latter, particularly IQM with SVM, shows promise by achieving an 8.97% equal error rate on high-quality Deepfakes.

Implications and Future Directions

The findings underscore the substantial threat posed by Deepfake technology to facial recognition systems. The high false acceptance rates indicate these systems struggle to differentiate real from manipulated content, highlighting an urgent need for more robust detection mechanisms.

• Practical Applications: Insecurity in face recognition systems can lead to vulnerabilities in security applications, authentication processes, and personal privacy. Ensuring the reliability of these systems against synthetic forgeries is imperative.
• Theoretical Advancements: The paper suggests that existing detection techniques, particularly those focused solely on visual data, may have limitations as face-swapping technology improves. Enhancements in detection algorithms need to encompass more sophisticated feature extraction and classification methodologies.
• Future Developments: The paper suggests a burgeoning arms race between generative and detection technologies. Future work could explore advanced techniques, such as deep learning models and multimodal data integration, to enhance detection capabilities. Additionally, incorporating subjective evaluations could provide insights into human detection abilities compared to automated systems.

Conclusion

Korshunov and Marcel's research provides essential insights into the current state of Deepfake technology and its impact on face recognition systems. By releasing a comprehensive dataset and evaluating current detection methodologies, the paper lays the groundwork for future research efforts aimed at countering the challenges posed by synthetic media. As Deepfake technology continues to evolve, ongoing research into detection and prevention remains critical to maintaining trust and security in digital media.