mlsanityData to 14 Oct 2026

Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces

arXiv 2205.06680 ↗First posted 13 May 2022cs.CVcited by 11

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Abstract (arXiv)

AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts. Although progress has been made in developing automatic methods to detect AI-synthesized faces, there is no open platform to study the human performance of AI-synthesized faces detection. In this work, we develop an online platform called Open-eye to study the human performance of AI-synthesized face detection. We describe the design and workflow of the Open-eye in this paper.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 13 May 2022.

Builds on

22 of 31 references.

References · 22

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  3. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  4. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  5. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  6. 2020Global Texture Enhancement for Fake Face Detection in the Wildcited by 220
  7. 2020Media Forensics and DeepFakes: an overviewcited by 209
  8. 2018Do GANs leave artificial fingerprints?cited by 190
  9. 2018Detecting GAN-generated Imagery using Color Cuescited by 76
  10. 2020SofGAN: A Portrait Image Generator with Dynamic Stylingcited by 65
  11. 2020Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlightscited by 59
  12. 2019Incremental learning for the detection and classification of GAN-generated imagescited by 58
10 more
  1. 2021Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Facescited by 48
  2. 2020Detecting CNN-Generated Facial Images in Real-World Scenarioscited by 32
  3. 2019Exposing GAN-synthesized Faces Using Landmark Locationscited by 30
  4. 2021Robust Attentive Deep Neural Network for Exposing GAN-generated Facescited by 28
  5. 2020Learning by Minimizing the Sum of Ranked Rangecited by 27
  6. 2021TkML-AP: Adversarial Attacks to Top-k Multi-Label Learningcited by 26
  7. 2020Detection, Attribution and Localization of GAN Generated Imagescited by 24
  8. 2021Sum of Ranked Range Loss for Supervised Learningcited by 23
  9. 2021More Real than Real: A Study on Human Visual Perception of Synthetic Facescited by 23
  10. 2018Identification of Deep Network Generated Images Using Disparities in Color Componentscited by 18

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 3.

11 papers.

  1. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  2. 2023Improving Fairness in Deepfake Detectioncited by 40
  3. 2024Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Imagescited by 20
  4. 2023Synthesizing Black-box Anti-forensics DeepFakes with High Visual Qualitycited by 12
  5. 2024Masked Conditional Diffusion Model for Enhancing Deepfake Detectioncited by 9
  6. 2023Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformationcited by 6
  7. 2025Robust AI-Generated Face Detection with Imbalanced Datacited by 5
  8. 2025Rethinking Individual Fairness in Deepfake Detectioncited by 4
  9. 2024A Self-Learning Multimodal Approach for Fake News Detectioncited by 3
  10. 2022Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflectioncited by 1
  11. 2025SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMscited by 0