mlsanityData to 14 Oct 2026

Why Do Facial Deepfake Detectors Fail?

arXiv 2302.13156 ↗First posted 25 Feb 2023cs.CR cs.LGcited by 12

Not on the map. Why?

Abstract (arXiv)

Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 25 Feb 2023.

Builds on

21 of 33 references.

References · 21

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
  4. 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 632
  5. 2019Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensicscited by 575
  6. 2021Multi-attentional Deepfake Detectioncited by 341
  7. 2022Detecting Deepfakes with Self-Blended Imagescited by 338
  8. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 280
  9. 2019Deferred Neural Rendering: Image Synthesis using Neural Texturescited by 253
  10. 2020The Creation and Detection of Deepfakes: A Surveycited by 247
  11. 2019FaceShifter: Towards High Fidelity And Occlusion Aware Face Swappingcited by 221
  12. 2022PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognitioncited by 67
9 more
  1. 2020Detecting Deep-Fake Videos from Appearance and Behaviorcited by 66
  2. 2021ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Imagescited by 48
  3. 2021FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learningcited by 44
  4. 2021CoReD: Generalizing Fake Media Detection with Continual Representation using Distillationcited by 37
  5. 2021One Detector to Rule Them All: Towards a General Deepfake Attack Detection Frameworkcited by 33
  6. 2022Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Eracited by 27
  7. 2021TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learningcited by 19
  8. 2021Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Imagescited by 9
  9. 2021DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Imagescited by 4

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 9.

12 papers.

  1. 2024SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Frameworkcited by 14
  2. 2024Disrupting Diffusion-based Inpainters with Semantic Digressioncited by 8
  3. 2024UGAD: Universal Generative AI Detector utilizing Frequency Fingerprintscited by 5
  4. 2025SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detectioncited by 5
  5. 2025LLMs Are Not Yet Ready for Deepfake Image Detectioncited by 4
  6. 2025Evaluating Deepfake Detectors in the Wildcited by 3
  7. 2025FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detectioncited by 3
  8. 2025Visual Language Models as Zero-Shot Deepfake Detectorscited by 2
  9. 2025From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Userscited by 2
  10. 2025Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detectioncited by 2
  11. 2025Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?cited by 1
  12. 2025A Multi-Level Strategy for Deepfake Content Moderation under EU Regulationcited by 0