Overview

Deepfake detection studies how to distinguish authentic images, video, and audio from manipulated or AI-generated content, a capability important for verification and security. Detectors look for spatial and frequency artifacts, temporal or physiological irregularities, and inconsistencies across audio, video, and semantic or physical cues; some also use pretrained foundation models, vision-language reasoning, or watermarks. Beyond classifying media, methods increasingly aim to localize altered regions or time spans, identify sources, and provide evidence-grounded explanations. A central challenge is that strong results on familiar benchmarks often fail to transfer to unseen generators, real-world conditions, or degraded media, while shortcuts, calibration, and fairness complicate reliable deployment.

Latest papers

CardsList

240 papers33 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

  1. Temporal Slice Learning for AI-Generated Video Detection with 400×\times Fewer FLOPs

    Oct 26, 2026Wei Shang, Zhiyuan Li, Shengfeng HeDeepfake DetectionSynthetic Video Detection Benchmarks

  2. Spectra: Multi-Scale Implicit Decomposition for Deepfake Detection

    Oct 22, 2026Dhananjaya Jayasundara, Kartik Narayan, Vishal M. PatelDeepfake Detection

  3. MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition

    Oct 22, 2026Wonjune Seo, Joonhyuk Baek, Yeseong Jung +1Adversarial AttacksTransfer-Based Adversarial Attacks

  4. Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos

    Oct 22, 2026Zhuoran Zhang, Yang Shi, Qixun Wang +11Synthetic Video Detection BenchmarksExplainable Deepfake Detection

  5. Beyond Shortcut Learning: Hierarchical Spatial Prototype Regularization for Face Forgery Detection

    Oct 21, 2026Chao Shuai, Shaojing Fan, Kun Pan +5Deepfake DetectionShortcut Learning

  6. From GAN-Based Models to GPT-5: A Taxonomy-Guided Study of Human Performance in Detecting Deepfake Face Images

    Oct 16, 2026Zhiqing Guo, Lixin Jia, Luning Sun +7Deepfake Detection

  7. Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection

    Oct 15, 2026Kajaree Das, Oliver Böse, Subarnaduti Paul +2Algorithmic FairnessDeepfake Detection

  8. Beyond Generation: Using Multimodal LLMs as Detectors of LLM-Generated Deepfake Facial Images

    Oct 15, 2026Olivia Salamon, Virginia N. L. Franqueira, Shujun LiAI-Generated Image DetectionMultimodal Deepfake Detection

  9. RLDF: A Region-Level Deepfake Dataset and Benchmark for Partial-Face Manipulation Detection

    Oct 15, 2026Shukesh Reddy, Abhijit DasDeepfake DetectionImage Forgery Localization

  10. Physics-Aware Deepfake Detection via Distance-Speech Consistency

    Oct 13, 2026Kyeongrae Kim, Kim Sung-Bin, Oh Hyun-Bin +1Deepfake DetectionAudio Deepfake Detection

  11. COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection

    Oct 13, 2026Zhan Yang, Tiancheng Shen, Weijie Lyu +4AI-Generated Image DetectionDeepfake Detection

  12. EviStress: Evidence Stress Learning for Generalizable Face Forgery Detection with Textual Explanations

    Oct 12, 2026Jiuyao Jing, Xuanyu Yi, Yu Zheng +1Image Forgery DetectionDeepfake Detection

  13. Not Every Cue Is Evidence: Cue Supportability for Forensic Distillation in Face Forgery Detection

    Oct 12, 2026Binjia Zhou, Dawei Luo, Shuai Chen +7Explainable Deepfake DetectionDeepfake Detection

  14. Does an Illumination Prior Help Face-Swap Detection? A Controlled Study of Temporal Self-Blended Images

    Oct 8, 2026Danil Davydov, Bader Rasheed, Dmitriy VatolinImage Forgery DetectionDeepfake Detection

  15. Camera-Noise Residuals for Face-Swap Detection: Redundant, Not Complementary, and Why

    Oct 8, 2026Danil Davydov, Bader Rasheed, Dmitriy VatolinDeepfake DetectionDigital Image Forensics

  16. Stop My Dancing! Understanding, Detecting and Attributing Motion-Aware Deepfake Videos

    Oct 8, 2026Fazhong Liu, Yan Meng, Tian Dong +2Deepfake DetectionDigital Forensics

  17. MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection

    Oct 7, 2026Artem Filippov, Aleksandr Gushchin, Kirill Koltsov +2Deepfake DetectionExplainable Deepfake Detection

  18. MOTIF: Person-of-Interest Deepfake Detection Beyond 3DMM Coefficients

    Oct 7, 2026Giovanni Affatato, Sara Mandelli, Paolo Bestagini +1Deepfake Detection3D Morphable Models

  19. Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses

    Oct 6, 2026Tianyi She, Jiawei Liu, Weifeng Liu +3Digital ForensicsDeepfake Detection

  20. CCDF: A Benchmark Dataset for Deepfake Detection in Surveillance Footage

    Oct 6, 2026Baptiste Chopin, Thomas Swearingen, Arun Ross +2Deepfake DetectionVideo Surveillance

  21. MTOR: Generalizable AI-Generated Video Detection with Multimodal Semantics and Temporal Over-Regularity

    Oct 5, 2026Hang Wang, Chao Shen, Lei Zhang +1Deepfake Detection

  22. BabelFake: A Multilingual Audio-Visual DeepFake Benchmark

    Oct 5, 2026Carlotta Segna, Joel Tschesche, Anna RohrbachAudio Deepfake DetectionDeepfake Detection

  23. Certification of Real Images through Calibrated Content Authentication

    Oct 5, 2026Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam +2Probability CalibrationDigital Image Forensics

  24. Interpretable Deepfake Detection in Videos via Explicit Forensic Features and Temporal Modeling

    Oct 2, 2026Chahira Benhama, Mohand Saïd Allili, Assia HamadeneVideo ForensicsDeepfake Detection

  25. Revisiting Cross-Reconstruction for Generalizable Deepfake Detection

    Oct 1, 2026Bingjian Yang, Shilei Zhao, Zheng WangImage Forgery DetectionDigital Image Forensics

  26. A Generalizable and Explainable Framework for Synthetic Video Detection Using First-Digit Gradient Statistics

    Sep 30, 2026Sidharth Shanu, Gautam Kumar, Tej SinghExplainable Deepfake DetectionDeepfake Detection