mlsanityData to 7 Oct 2026

Al'em do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes

arXiv 2601.08674 ↗First posted 13 Jan 2026cs.CV cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Deepfakes are synthetic media generated by artificial intelligence, with positive applications in education and creativity, but also serious negative impacts such as fraud, misinformation, and privacy violations. Although detection techniques have advanced, comprehensive evaluation methods that go beyond classification performance remain lacking. This paper proposes a reliability assessment framework based on four pillars: transferability, robustness, interpretability, and computational efficiency. An analysis of five state-of-the-art methods revealed significant progress as well as critical limitations.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 13 Jan 2026.

Builds on

27 of 50 references.

References · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  6. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
  7. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 318
  8. 2021SimSwap: An Efficient Framework For High Fidelity Face Swappingcited by 243
  9. 2019FaceShifter: Towards High Fidelity And Occlusion Aware Face Swappingcited by 221
  10. 2021WildDeepfake: A Challenging Real-World Dataset for Deepfake Detectioncited by 207
  11. 2024X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attentioncited by 88
  12. 2022DiffFace: Diffusion-based Face Swapping with Facial Guidancecited by 77
15 more
  1. 2022FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbationscited by 75
  2. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 66
  3. 2020Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systemscited by 65
  4. 2023Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detectioncited by 50
  5. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  6. 2021Fighting deepfakes by detecting GAN DCT anomaliescited by 39
  7. 2025Efficient Diffusion Models: A Surveycited by 35
  8. 2021One Detector to Rule Them All: Towards a General Deepfake Attack Detection Frameworkcited by 33
  9. 2023Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptationcited by 16
  10. 2023Computation-efficient Deep Learning for Computer Vision: A Surveycited by 14
  11. 2024On the Exploitation of DCT-Traces in the Generative-AI Domaincited by 9
  12. 2024Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixturecited by 7
  13. 2024Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Mediacited by 4
  14. 2024Data-Driven Fairness Generalization for Deepfake Detectioncited by 4
  15. 2023Scapegoat Generation for Privacy Protection from Deepfakecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Benchmarking Spatial, Spectral, and Self-Supervised Cues for Face Forgery Detection under Realistic Degradationcited by 0