DeepFake Detection
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Benchmarks
FakeAVCeleb
FaceForensics++
DFDC
FaceForensics
1
COCOFake
DFFD
LAV-DF
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Most implemented
FaceForensics++: Learning to Detect Manipulated Facial Images
Taming Transformers for High-Resolution Image Synthesis
The DeepFake Detection Challenge (DFDC) Dataset
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
MesoNet: a Compact Facial Video Forgery Detection Network
Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
Papers
Revisiting Cross-Reconstruction for Generalizable Deepfake Detection
Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable forensic cues. Recent cross-reconstruction based methods attempt to impr…
DeepFake DetectionUnifying Semantic Priors and High-Frequency Traces: Enhancing V-JEPA with Mixture-of-Experts for Robust Synthetic Image Forensics
The unchecked proliferation of manipulated images on social media platforms has increased the spread of misinformation, posing a severe threat to public trust and information integrity. Modern deepfake detectors typicall…
Multiple Instance LearningDeepFake DetectionFrom Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection
Speech deepfakes can mimic a speaker's voice convincingly enough to deceive listeners and automated systems. This has driven strong progress in speech deepfake detection, but most detectors still end with one score per u…
DeepFake DetectionAdaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments
Deepfake detection models often rely on high-quality inputs, fixed inference paths, and computationally expensive architectures, limiting their use in low-resolution and resource-constrained settings. This paper proposes…
Computational EfficiencyDeepFake DetectionDF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts
Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deepfake generation methods. We conjecture that overfitting can be mitigate…
DeepFake DetectionFace ParsingExplainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization
Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope is critical in practice, where users li…
Binary ClassificationContrastive LearningDeepFake Detection