Unmasking DeepFakes with simple Features

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

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Cited by in corpus (18)

• Fighting deepfakes by detecting GAN DCT anomalies
• FedForgery: Generalized Face Forgery Detection with Residual Federated Learning
• Deepfake Detection using Spatiotemporal Convolutional Networks
• Identifying and Mitigating the Security Risks of Generative AI
• Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review
• FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects
• Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
• Generalizing Face Forgery Detection with High-frequency Features
• Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
• Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
• Responsible Disclosure of Generative Models Using Scalable Fingerprinting
• Exposing Deep-faked Videos by Anomalous Co-motion Pattern Detection
• D2Fusion: Dual-domain Fusion with Feature Superposition for Deepfake Detection
• On the detection of morphing attacks generated by GANs
• A Note on Deepfake Detection with Low-Resources
• Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
• Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed
• Self-supervised GAN Detector