Towards Sustainable Universal Deepfake Detection with Frequency-Domain Masking

TL;DR

This paper proposes a frequency-domain masking training strategy for deepfake detection that improves generalization across diverse generative models and maintains robustness under model pruning, promoting sustainable large-scale screening.

Contribution

It introduces frequency masking as a novel training technique that enhances deepfake detector generalization and robustness while reducing computational requirements.

Findings

Achieves state-of-the-art generalization on GAN and diffusion datasets.

Maintains performance under significant model pruning.

Offers a scalable, resource-efficient detection method.

Abstract

Universal deepfake detection aims to identify AI-generated images across a broad range of generative models, including unseen ones. This requires robust generalization to new and unseen deepfakes, which emerge frequently, while minimizing computational overhead to enable large-scale deepfake screening, a critical objective in the era of Green AI. In this work, we explore frequency-domain masking as a training strategy for deepfake detectors. Unlike traditional methods that rely heavily on spatial features or large-scale pretrained models, our approach introduces random masking and geometric transformations, with a focus on frequency masking due to its superior generalization properties. We demonstrate that frequency masking not only enhances detection accuracy across diverse generators but also maintains performance under significant model pruning, offering a scalable and resource-conscious solution. Our method achieves state-of-the-art generalization on GAN- and diffusion-generated image datasets and exhibits consistent robustness under structured pruning. These results highlight the potential of frequency-based masking as a practical step toward sustainable and generalizable deepfake detection. Code and models are available at https://github.com/chandlerbing65nm/FakeImageDetection.