Anti-Compression Contrastive Facial Forgery Detection

TL;DR

This paper introduces a contrastive learning framework that enhances facial forgery detection robustness against various levels of video compression by aligning representations of weakly and strongly compressed data.

Contribution

It proposes a novel anti-compression detection method using relation-based contrastive learning to improve detection accuracy across different compression levels.

Findings

Boosts detection accuracy for strongly compressed videos.

Improves overall forgery detection performance on compressed data.

Enhances model robustness against lossy compression artifacts.

Abstract

Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We believe creating a forgery detection model that can handle the data compressed with unknown levels is important. To enhance the performance for such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, the algorithm measures the pair-wise similarity within data as the relations, and forcing the relations of weak and strong compressed data close to each other, thus improving the discriminate power for detecting strong compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video level contrastive learning for weak compressed data, which forces the model to produce similar representations within the same video and far from the negative samples. The experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting the clean data.