ID-Reveal: Identity-aware DeepFake Video Detection (2012.02512v3)

Abstract: A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.

• The paper introduces a novel DeepFake detection method that focuses on identity-specific temporal facial features.
• It employs a Temporal ID Network and adversarial training to build robust biometric models from authentic video data.
• The approach outperforms existing methods, achieving over 15% accuracy improvement on highly compressed videos.

ID-Reveal: Identity-aware DeepFake Video Detection

The paper "ID-Reveal: Identity-aware DeepFake Video Detection" introduces a novel technique for detecting DeepFake videos by focusing on the identity-specific dynamics of a person's facial movements. This research addresses a critical issue in the domain of media forensics—improving the generalization capability of DeepFake detection methods across different types of forgeries, such as face swapping and facial reenactment, which has been a persistent challenge due to the diversity and evolution of forgery techniques.

The ID-Reveal approach proposes a framework that relies solely on authentic video content for training while learning temporal facial features representative of a person's unique speaking and movement patterns. This method leverages metric learning in conjunction with adversarial training to develop a robust model that can discern DeepFakes without necessitating exposure to manipulated data during the training phase.

Key components of the ID-Reveal framework include:

• Temporal Facial Feature Analysis: The approach captures high-level semantic facial expressions over time using a three-dimensional morphable model (3DMM). This involves parameterizing a face into coefficients for shape, expression, appearance, and pose, preserving temporal dynamics essential for identity verification.
• Temporal ID Network: This neural network processes sequences of 3DMM features to generate embeddings by examining temporal patterns that are indicative of unique identity traits. These embeddings allow the computation of a distance metric against pristine videos of a given subject to identify potentially manipulated videos.
• Adversarial Network Training: An adversarial component governed by a generative network is trained to produce features imitating fake content, enhancing the Temporal ID Network's focus on temporal rather than static cues. This adversarial training pushes the Temporal ID Network to refine its ability to differentiate between real and fake sequences based on movement patterns rather than static appearance alone.

Experimental results highlight the efficacy of ID-Reveal, showcasing an impressive generalization across different synthetic manipulation techniques and resilience to low-quality video alterations common on social networks. For instance, in facial reenactment detection on highly compressed videos, ID-Reveal achieved an average accuracy improvement exceeding 15% over state-of-the-art methods.

The implications of ID-Reveal are significant both theoretically and practically. Theoretically, it advances DeepFake detection techniques by shifting focus from static image artifacts to dynamic biometric features. Practically, it provides a scalable and adaptive solution to detecting newly emerging forgery methods without the need for extensive retraining on manipulated datasets.

Future developments in AI and DeepFake technology will likely continue to evolve, challenging detectors with new types of forgeries. Approaches like ID-Reveal that leverage biometric characteristics and identity dynamics offer a promising path forward, emphasizing the need for adaptable, forgery-agnostic systems in media forensics. Further research might explore expanding this framework to real-time applications and integrating multi-modal data to enhance detection robustness.