Towards General Deepfake Detection with Dynamic Curriculum

TL;DR

This paper introduces a curriculum learning approach called DFFC that dynamically emphasizes hard samples during training, significantly enhancing the generalization and robustness of deepfake detectors across datasets.

Contribution

It proposes a novel dynamic curriculum strategy that integrates facial quality and loss to focus on hard samples, improving deepfake detection performance.

Findings

Enhanced cross-dataset detection accuracy

Improved learning of general forgery features

Effective plug-and-play training strategy

Abstract

Most previous deepfake detection methods bent their efforts to discriminate artifacts by end-to-end training. However, the learned networks often fail to mine the general face forgery information efficiently due to ignoring the data hardness. In this work, we propose to introduce the sample hardness into the training of deepfake detectors via the curriculum learning paradigm. Specifically, we present a novel simple yet effective strategy, named Dynamic Facial Forensic Curriculum (DFFC), which makes the model gradually focus on hard samples during the training. Firstly, we propose Dynamic Forensic Hardness (DFH) which integrates the facial quality score and instantaneous instance loss to dynamically measure sample hardness during the training. Furthermore, we present a pacing function to control the data subsets from easy to hard throughout the training process based on DFH. Comprehensive experiments show that DFFC can improve both within- and cross-dataset performance of various kinds of end-to-end deepfake detectors through a plug-and-play approach. It indicates that DFFC can help deepfake detectors learn general forgery discriminative features by effectively exploiting the information from hard samples.