Główne pojęcia
A challenge-response approach can effectively detect real-time deepfakes by exploiting inherent limitations of deepfake generation pipelines.
Streszczenie
The article explores a challenge-response approach for authenticating live video interactions and developing a taxonomy of challenges that target vulnerabilities in real-time deepfake (RTDF) generation pipelines. The authors collected a unique dataset of 56,247 videos from 47 participants performing eight challenges, which consistently and visibly degrades the quality of state-of-the-art deepfake generators. Both human and automated evaluations corroborate these findings, demonstrating the promising potential of challenge-response systems for explainable and scalable real-time deepfake detection.
The key components of an RTDF generation pipeline are discussed, including face detection, landmark detection, face alignment, segmentation, face-swapping, blending, and color correction. The authors leverage the inherent limitations of these components, such as data diversity, face shape similarity, computational resources, and real-time constraints, to design effective challenges.
The taxonomy of challenges includes head movements, face occlusions, facial deformations, and face illumination changes. The authors collect a dataset of original and deepfake videos for each challenge and evaluate them using both human assessments and an automated scoring model. The results show that challenges can consistently and visibly degrade the quality of deepfakes, with the human-based evaluation achieving an AUC of 88.6% and the automated evaluation reaching 80.1% AUC.
The findings underscore the potential of challenge-response systems for practical, explainable, and scalable real-time deepfake detection. The authors discuss the limitations of the approach, such as the need for savvy imposters to adapt to the challenges, and the defenders' limited situational awareness.
Statystyki
The dataset consists of 56,247 videos, including 409 original videos and 55,838 deepfake videos generated using three RTDF pipelines (LIA, FSGAN, and DFL).
Cytaty
"RTDFs have already become prevalent to the extent that the FBI has warned of their imminent threat and pervasiveness."
"Conventional techniques have considered deepfake detection, but in an offline and non-interactive setting. Despite being technically impressive, such techniques are not explicitly designed for RTDFs and operate under the assumption of no interaction between an imposter and the detector."
"We leverage this asymmetric advantage to design and validate a challenge-response approach for identifying RTDFs."