Quality-Agnostic Deepfake Detection With Saliency-Guided Restoration and Adaptive Fusion

Deepfake technology poses a significant threat to the Internet of Things by enabling identity spoofing and the dissemination of misinformation. Although numerous face forgery detectors have been developed to counter the risks of facial deepfakes, detecting forgeries across varying quality levels, particularly under extreme degradations, remains a critical challenge. Current face forgery detection methods largely rely on identifying low-level artifacts, which are highly susceptible to distortions introduced by image degradation. Recognizing that restoration can recover critical forensic cues from severely degraded forgeries, this study proposes a quality-agnostic deepfake detection framework that leverages a blind face restoration model to enhance robustness. The framework incorporates an auxiliary restoration branch alongside the original detection pathway. While the original branch operates directly on the degraded input, the restoration branch performs detection on facial images restored by a blind face restoration model. In addition, a Saliency-Guided Restoration objective is introduced to enhance alignment between the restoration and detection tasks. A Restoration Similarity-Aware Fusion mechanism is further designed to adaptively integrate predictions from both branches based on input quality. To assess the robustness of our approach under extreme degradation, we establish a specialized, real-world-inspired benchmark that simulates diverse degradation scenarios. Comprehensive experiments on both the proposed and existing benchmarks demonstrate that our method consistently achieves superior robustness in various degradation scenarios.