FakeFormer: Efficient Vulnerability-Driven Transformers for Generalisable Deepfake Detection

TL;DR

FakeFormer enhances Vision Transformers for deepfake detection by focusing on artifact-vulnerable regions, leading to better generalization and efficiency without large datasets.

Contribution

The paper introduces FakeFormer, a novel ViT-based framework that explicitly learns to detect subtle forgery artifacts, improving deepfake detection performance.

Findings

Outperforms state-of-the-art on multiple datasets

Requires less training data for effective detection

Achieves better generalization and computational efficiency

Abstract

Recently, Vision Transformers (ViTs) have achieved unprecedented effectiveness in the general domain of image classification. Nonetheless, these models remain underexplored in the field of deepfake detection, given their lower performance as compared to Convolution Neural Networks (CNNs) in that specific context. In this paper, we start by investigating why plain ViT architectures exhibit a suboptimal performance when dealing with the detection of facial forgeries. Our analysis reveals that, as compared to CNNs, ViT struggles to model localized forgery artifacts that typically characterize deepfakes. Based on this observation, we propose a deepfake detection framework called FakeFormer, which extends ViTs to enforce the extraction of subtle inconsistency-prone information. For that purpose, an explicit attention learning guided by artifact-vulnerable patches and tailored to ViTs is introduced. Extensive experiments are conducted on diverse well-known datasets, including FF++, Celeb-DF, WildDeepfake, DFD, DFDCP, and DFDC. The results show that FakeFormer outperforms the state-of-the-art in terms of generalization and computational cost, without the need for large-scale training datasets. The code is available at \url{https://github.com/10Ring/FakeFormer}.