ProFake: Detecting Deepfakes in the Wild against Quality Degradation with Progressive Quality-adaptive Learning

Despite the promising advances in deepfake detection on current datasets, detecting visual deepfakes in real-world scenarios (e.g., deepfake videos and live streaming on YouTube) remains a challenge due to the inherent quality degradation such as unpredictable compression employed by social media platforms. Such degradation perturbs discernible forgery clues and diminishes the effectiveness of deepfake detection methods, raising a critical safety concern to the misuse of forgery faces in real-world scenarios. In this paper, we aim to understand the impacts of real-world degradation on the robustness of deepfake detection. Particularly, we investigate the risk of degraded deepfakes towards their detection on two real-world scenarios (i.e., deepfake videos and deepfake live streaming on social media platforms). By measuring the effects of real-world degradations on the performance and representation capabilities of detection models, we reveal that real-world deepfakes can be simulated via common degradation operations (e.g., JPEG compression) as they are perceptually similar to deepfake detectors. By analyzing the training dynamics under different sequences of training samples, we observe that the training order of deepfakes progressing from non-degraded (easy) to heavily degraded (hard) enhances the adaptability of detection models to various degradation in real-world scenarios. Drawing from these observations, we present a novel deepfake detection method ProFake to enhance the robustness of deepfake detection against real-world quality degradations. ProFake enables quality-adaptive learning via progressively degrade, detect and assign weights for the training samples driven by the feedback of model performance and image quality, which ensures that our model gradually focuses on more challenging samples to achieve quality-adaptive deepfake detection. Extensive experiments show that compared with existing methods, ProFake improves deepfake detection accuracy by an average of over 10 % in real-world scenarios and by an average of over 30 % in heavily degraded scenarios, while maintaining comparable performance in detecting high-quality deepfakes.