DPL: Cross-quality DeepFake Detection via Dual Progressive Learning

TL;DR

This paper introduces a Dual Progressive Learning framework that enhances DeepFake detection across varying video qualities by progressively excavating forgery traces through sequential branches, improving detection accuracy in challenging scenarios.

Contribution

The novel DPL framework employs dual sequential branches with a CLIP-based indicator and feature selection to adaptively detect DeepFakes across different video qualities.

Findings

Outperforms existing methods in cross-quality DeepFake detection

Effectively excavates forgery traces in low-quality videos

Maintains reasonable memory costs with progressive learning

Abstract

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. 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 takes advantage of the sequential model where computational units share weights across different time steps and can memorize previous progress, elegantly achieving progressive learning while maintaining reasonable memory costs. Extensive experiments demonstrate the superiority of our method for cross-quality DeepFake detection.