mlsanityData to 14 Oct 2026

Towards Universal GAN Image Detection

arXiv 2112.12606 ↗First posted 23 Dec 2021cs.CVcited by 26

Not on the map. Why?

Abstract (arXiv)

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 23 Dec 2021.

Builds on

24 of 31 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  7. 2020Supervised Contrastive Learningcited by 2,829
  8. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  9. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  10. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,039
  11. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  12. 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 504
12 more
  1. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  2. 2020What makes fake images detectable? Understanding properties that generalizecited by 273
  3. 2019On the Detection of Digital Face Manipulationcited by 260
  4. 2020Media Forensics and DeepFakes: an overviewcited by 209
  5. 2018Do GANs leave artificial fingerprints?cited by 190
  6. 2021Are GAN generated images easy to detect? A critical analysis of the state-of-the-artcited by 139
  7. 2018ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detectioncited by 104
  8. 2019Incremental learning for the detection and classification of GAN-generated imagescited by 58
  9. 2019RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributescited by 56
  10. 2019On the generalization of GAN image forensicscited by 36
  11. 2021More Real than Real: A Study on Human Visual Perception of Synthetic Facescited by 23
  12. 2019A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detectioncited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 7; 2023: 6; 2024: 6; 2025: 6; 2026: 1.

26 papers.

Sort:

By area:

  1. 2024AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Errorcited by 125
  2. 2022Towards the Detection of Diffusion Model Deepfakescited by 111
  3. 2022Fusing Global and Local Features for Generalized AI-Synthesized Image Detectioncited by 88
  4. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 74
  5. 2023Generalizable Synthetic Image Detection via Language-guided Contrastive Learningcited by 49
  6. 2022Detecting GAN-generated Images by Orthogonal Training of Multiple CNNscited by 46
  7. 2024Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasetscited by 43
  8. 2023Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysiscited by 30
  9. 2023Parents and Children: Distinguishing Multimodal DeepFakes from Natural Imagescited by 29
  10. 2022GLFF: Global and Local Feature Fusion for AI-synthesized Image Detectioncited by 25
  11. 2023Weakly-supervised deepfake localization in diffusion-generated imagescited by 24
  12. 2023On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlookcited by 15
14 more
  1. 2024GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learningcited by 12
  2. 2024AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Imagescited by 10
  3. 2025Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters?cited by 10
  4. 2022An Overview of Recent Work in Media Forensics: Methods and Threatscited by 7
  5. 2022DepthFake: a depth-based strategy for detecting Deepfake videoscited by 5
  6. 2024UGAD: Universal Generative AI Detector utilizing Frequency Fingerprintscited by 5
  7. 2025RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectorscited by 4
  8. 2024Landscape More Secure Than Portrait? Zooming Into the Directionality of Digital Images With Security Implicationscited by 2
  9. 2026Intermediate Representations are Strong AI-Generated Image Detectorscited by 2
  10. 2023Sequential training of GANs against GAN-classifiers reveals correlated "knowledge gaps" present among independently trained GAN instancescited by 1
  11. 2025Synthetic Art Generation and DeepFake Detection A Study on Jamini Roy Inspired Datasetcited by 1
  12. 2025Advance Fake Video Detection via Vision Transformerscited by 1
  13. 2025PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Imagescited by 1
  14. 2025Maximize margins for robust splicing detectioncited by 0