Towards reliable deepfake detection from uncertainty calibration perspective

The rapid development of face forgery technology has raised concerns among researchers about information security. To mitigate the malicious abuse of the technology, deepfake detection has proven to be an effective countermeasure. However, existing methods only provide a true-false and binary classification, and they lack research on uncertainty estimation, which leads to the predictive reliability being unknown. The model uncertainty reflects its understanding of knowledge. Even when faced with unseen deepfake content, a detector without awareness of its cognitive limitations may still output incorrect predictions with high confidence. The detector assigns extremely high probabilities to samples, which is referred to a phenomenon known as overconfidence. In this paper, we introduce the novel concept of deepfake detection calibration, which focuses on aligning predicted probability with actual distribution to alleviate the overconfidence and estimate the uncertainty. Specifically, we introduce an innovative calibration method called packed-ensembles (PE), which divides a detector into multiple subnetworks. PE allows each subnetwork to focus on different forgery traces, resulting in reliable predictions without incurring additional computational costs. Comprehensive experiments demonstrate that our method effectively mitigates the overconfidence in deepfake detection while maintaining generalization, and significantly enhances detection reliability.