DPL: Cross-quality DeepFake Detection via Dual Progressive Learning

Dongliang Zhang, Yunfei Li, Jiaran Zhou, Yuezun Li; Proceedings of the Asian Conference on Computer Vision (ACCV), 2024, pp. 1116-1132

Real-world DeepFake videos often undergo various compression operations, resulting in a range of video qualities. These varying qualities diversify the pattern of forgery traces, significantly increasing the difficulty of DeepFake detection. To address this challenge, we introduce a new Dual Progressive Learning (DPL) framework for cross-quality DeepFake detection. Specifically, we liken this task to progressively drilling for underground water, where low-quality videos require more effort than high-quality ones. To achieve this, we develop two sequential-based branches to "drill waters" with different efforts. The first branch progressively excavates the forgery traces according to the levels of video quality, i.e., time steps, determined by a dedicated CLIP-based indicator. In this branch, a Feature Selection Module is designed to adaptively assign appropriate features to the corresponding time steps. Considering that different techniques may introduce varying forgery traces within the same video quality, we design a second branch targeting forgery identifiability as complementary. This branch operates similarly and shares the feature selection module with the first branch. Our design elegantly achieves progressive learning, as the sequential model is inherently dynamic with weight-sharing units across different time steps and can memorize the previous progress, reducing memory costs while retaining effectiveness. Extensive experiments demonstrate the superiority of our method for cross-quality DeepFake detection.