Unmasking DeepFakes with simple Features

TL;DR

This paper presents a simple frequency domain analysis method for detecting DeepFake images and videos, achieving high accuracy with minimal labeled data and in unsupervised scenarios.

Contribution

The authors introduce a straightforward frequency-based approach that outperforms previous methods, especially in low-data and unsupervised settings.

Findings

Achieves 100% accuracy on high-resolution face images with only 20 labeled samples.

Reaches 96% accuracy in unsupervised detection on medium-resolution images.

Detects manipulated videos with 91% accuracy in low-resolution datasets.

Abstract

Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets. Due to this improvement, fake digital contents have proliferated growing concern and spreading distrust in image content, leading to an urgent need for automated ways to detect these AI-generated fake images. Despite the fact that many face editing algorithms seem to produce realistic human faces, upon closer examination, they do exhibit artifacts in certain domains which are often hidden to the naked eye. In this work, we present a simple way to detect such fake face images - so-called DeepFakes. Our method is based on a classical frequency domain analysis followed by basic classifier. Compared to previous systems, which need to be fed with large amounts of labeled data, our approach showed very good results using only a few annotated training samples and even achieved good accuracies in fully unsupervised scenarios. For the evaluation on high resolution face images, we combined several public datasets of real and fake faces into a new benchmark: Faces-HQ. Given such high-resolution images, our approach reaches a perfect classification accuracy of 100% when it is trained on as little as 20 annotated samples. In a second experiment, in the evaluation of the medium-resolution images of the CelebA dataset, our method achieves 100% accuracy supervised and 96% in an unsupervised setting. Finally, evaluating a low-resolution video sequences of the FaceForensics++ dataset, our method achieves 91% accuracy detecting manipulated videos. Source Code: https://github.com/cc-hpc-itwm/DeepFakeDetection