Awesome-AIGCDetection

Dataset

• WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection

  • Yan Hong, Jianming Feng, Haoxing Chen, Jun Lan, Huijia Zhu, Weiqiang Wang, and Jianfu Zhang. arXiv, 2024.

• GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image

  • Mingjian Zhu, Hanting Chen, Qiangyu Yan, Xudong Huang, Guanyu Lin, Wei Li, Zhijun Tu, Hailin Hu, Jie Hu, Yunhe Wang. arXiv, 2023.

• CNN-generated images are surprisingly easy to spot...for now [code]

  • Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros. CVPR, 2020.

2025

ICML 2025

• Are High-Quality AI-Generated Images More Difficult for Models to Detect? [code]

• Few-Shot Learner Generalizes Across AI-Generated Image Detection [code]

• Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection [code]

• Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection [code]

• Unlocking the Capabilities of Large Vision-Language Models for Generalizable and Explainable Deepfake Detection

CVPR 2025

• Any-Resolution AI-Generated Image Detection by Spectral Learning [code]

• A Bias-Free Training Paradigm for More General AI-generated Image Detection [code]

• Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network [code]

• FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error

• Secret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis

• Community Forensics: Using Thousands of Generators to Train Fake Image Detectors [code]

• SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model [code]

• CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI

• D^3: Scaling Up Deepfake Detection by Learning from Discrepancy [code]

ICLR 2025

• Aligned Datasets Improve Detection of Latent Diffusion-Generated Images [code]

• A Sanity Check for AI-generated Image Detection [code]

• LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models [code]

ICCV 2025

• LEGION: Learning to Ground and Explain for Synthetic Image Detection [code]

• OTA: Bit-Planes Guided AI-Generated Image Detection

• Forensic-MoE: Exploring Comprehensive Synthetic Image Detection Traces with Mixture of Experts

• Semantic Discrepancy-aware Detector for Image Forgery Identification

• AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models [code]

• Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection

• Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

• Diffusion Epistemic Uncertainty with Asymmetric Learning for Diffusion-Generated Image Detection

• ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection [code]

2024

• FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusion [page]

  • George Cazenavette, Avneesh Sud, Thomas Leung and Ben Usman. CVPR, 2024.

• Whodunit: Detection and Attribution of Synthetic Images by Leveraging Model-specific Fingerprints [code]

  • Alexander Wißmann, Steffen Zeiler, Robert M. Nickel, and Dorothea Kolossa. MAD, 2024.

• Exposing the Fake: Effective Diffusion-Generated Images Detection

  • Ruipeng Ma, Jinhao Duan, Fei Kong, Xiaoshuang Shi, Kaidi Xu. ICML, 2024.

• How to Trace Latent Generative Model Generated Images without Artificial Watermark? [code]

  • Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma. ICML, 2024.

• DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images [dataset]

  • Baoying Chen, Jishen Zeng, Jianquan Yang, Rui Yang. ICML*, 2024.

• CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection [code]

  • Sohail Ahmed Khan and Duc-Tien Dang-Nguyen. ICMR, 2024.

• Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks [code]

  • Romeo Lanzino, Federico Fontana, Anxhelo Diko, Marco Raoul Marini, Luigi Cinque. CVPR Workshop, 2024.

• Enhancing the Generalization of Synthetic Image Detection Models through the Exploration of Features in Deep Detection Models

  • Alireza Hajabdollah Javaheri, Hossein Motamednia, Ahmad Mahmoudi-Azanveh.MVIP, 2024.

• Forensic analysis of AI-compression traces in spatial and frequency domain

  • Sandra Bergmann, Denise Moussa, Fabian Brand, André Kaup, Christian Riess. Pattern Recognition Letters.

• Masked conditional diffusion model for enhancing deepfake detection

  • Tiewen Chen, Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, Xin Wang

• Did You Note My Palette? Unveiling Synthetic Images Through Color Statistics

  • Lea Uhlenbrock, Davide Cozzolino, Denise Moussa, Luisa Verdoliva, and Christian Riess. IH&MMSec, 2024.

• DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection [code]

  • Yewon Lim, Changyeon Lee, Aerin Kim, Oren Etzioni.

• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection

  • Zhiyuan He, Pin-Yu Chen and Tsung-Yi Ho.

• Text Modality Oriented Image Feature Extraction for Detecting Diffusion-based DeepFake

  • Di Yang, Yihao Huang, Qing Guo, Felix Juefei-Xu, Xiaojun Jia, Run Wang, Geguang Pu, and Yang Liu.

• Diffusion Noise Feature: Accurate and Fast Generated Image Detection [code]

  • Yichi Zhang and Xiaogang Xu.

• Improving Interpretability and Robustness for the Detection of AI-Generated Images

  • Tatiana Gaintseva, Laida Kushnareva, German Magai, Irina Piontkovskaya, Sergey Nikolenko, Marting Benning,Serguei Barannikov, Gregory Slabaugh.

• Development of a Dual-Input Neural Model for Detecting AI-Generated Imagery

  • Jonathan Gallagher, William Pugsley.

• Continuous fake media detection: adapting deepfake detectors to new generative techniques

  • Francesco Tassone, Luca Maiano, Irene Amerini.

• Detecting AI-Generated Images via CLIP

  • Alexander Moskowitz, Tyler Gaona and Jacob Peterson.

• Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection [code]

  • Chuangchuang Tan, Huan Liu, Yao Zhao, Shikui Wei, Guanghua Gu, Ping Liu, Yunchao Wei. CVPR, 2024.

• LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection

  • Yunpeng Luo, Junlong Du, Ke Yan, Shouhong Ding. CVPR, 2024.

• [Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection][https://arxiv.org/pdf/2403.13479]

  • Davide Alessandro Coccomini, Roberto Caldelli, Claudio Gennaro, Giuseppe Fiameni, Giuseppe Amato, Fabrizio Falchi. arXiv, 2024.

• Artifact Feature Purification for Cross-domain Detection of AI-generated Images

  • Zheling Meng, Bo Peng, Jing Dong, Tieniu Tan. arXiv, 2024.

• Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?

  • Anna Yoo Jeong Ha, Josephine Passananti, Ronik Bhaskar, Shawn Shan, Reid Southen, Haitao Zheng, Ben Y. Zhao. arXiv, 2024.

• Let Real lmages be as a Judger, Spotting Fake lmagesSynthesized with GenerativeModels

  • Ziyou Liang, Run Wang, Weifeng Liu, Yuyang Zhang, Wenyuan Yang, Lina Wang, Xingkai Wang. arXiv, 2024.

• D^3 Scaling Up Deepfake Detection by Learning from Discrepancy

  • Yongqi Yang, Zhihao Qian, Ye Zhu, Yu Wu. arXiv, 2024.

• Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection [code]

  • Chuangchuang Tan, Ping Liu, RenShuai Tao, Huan Liu, Yao Zhao, Baoyuan Wu, Yunchao Wei. arXiv, 2024.

• Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets [page]

  • Patrick Grommelt, Louis Weiss, Franz-Josef Pfreundt, Janis Keuper. arXiv, 2024.

• Bi-LORA: A Vision-Language Approach for Synthetic Image Detection

  • Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene, Abdenour Hadid, Abdelmalik Taleb-Ahmed. arXiv, 2024.

• Harnessing the Power of Large Vision Language Models for Synthetic Image Detection [code]

  • Mamadou Keita, Wassim Hamidouche, Hassen Bougueffa, Abdenour Hadid, Abdelmalik Taleb-Ahmed. arXiv, 2024.

• Mixture of Low-rank Experts for Transferable AI-Generated Image Detection [code]

  • Zihan Liu, Hanyi Wang, Yaoyu Kang, Shilin Wang. arXiv, 2024.

• Detecting Image Attribution for Text-to-Image Diffusion Models in RGB and Beyond [code]

  • Katherine Xu, Lingzhi Zhang, Jianbo Shi. arXiv, 2024.

• On the Exploitation of DCT-Traces in the Generative-AI Domain

  • Orazio Pontorno, Luca Guarnera, and Sebastiano Battiato. arXiv, 2024.

• A Single Simple Patch is All You Need for AI-generated Image Detection

  • Jiaxuan Chen, Jieteng Yao, and Li Niu. arXiv, 2024.

• MDTL-NET: Computer-generated image detection based on multi-scale deep texture learning

  • Qiang Xu, Shan Jia, Xinghao Jiang, Tanfeng Sun, Zhe Wang, and Hong Yan. Expert Systems with Applications, 2024.

• AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error

  • Jonas Ricker, Denis Lukovnikov, and Asja Fischer. CVPR, 2024.

• Frequency Masking for Universal Deepfake Detection [code]

  • Chandler Timm Doloriel, Ngai-Man Cheung. arXiv, 2024.

2023

• Generalizable Synthetic Image Detection via Language-guided Contrastive Learning [code]

  • Haiwei Wu, Jiantao Zhou, and Shile Zhang. arXiv, 2023.

• Synthbuster: Towards Detection of Diffusion Model Generated Images

  • Quentin Bammey. IEEE Open Journal of Signal Processing.

• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection

  • Huan Liu, Zichang Tan, Chuangchuang Tan, Yunchao Wei, Yao Zhao, Jingdong Wang. CVPR, 2024.

• Disentangling Different Levels of GAN Fingerprints for Task-specific Forensics

  • Chi Liu, Tianqing Zhu, Yuan Zhao, Jun Zhang, Wanlei Zhou. Computer Standards & Interfaces, 2023.

• AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors

  • You-Ming Chang, Chen Yeh, Wei-Chen Chiu, Ning Yu. arXiv.

• Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis [code]

  • Sergey Sinitsa, Ohad Fried. WACV, 2024

• Detecting Generated Images by Real Images Only

  • Xiuli Bi, Bo Liu, Fan Yang, Bin Xiao, Weisheng Li, Gao Huang, Pamela C. Cosman. arXiv.

• X-Transfer: A Transfer Learning-Based Framework for Robust GAN-Generated Fake Image Detection

  • Lei Zhang, Hao Chen, Shu Hu, Bin Zhu, Xi Wu, Jinrong Hu, Xin Wang. arXiv.

• Online Detection of AI-Generated Images

  • David C. Epstein, Ishan Jain, Oliver Wang, Richard Zhang. ICCV Workshop, 2023.

• Detecting Images Generated by Deep Diffusion Models Using Their Local Intrinsic Dimensionality

  • Peter Lorenz, Ricard L. Durall, Janis Keuper. ICCV Workshop, 2023.

• GenDet: Towards Good Generalizations for AI-Generated Image Detection

  • Mingjian Zhu, Hanting Chen, Mouxiao Huang, Wei Li, Hailin Hu, Jie Hu, Yunhe Wang. arXiv.

• Rich and Poor Texture Contrast: A Simple yet Effective Approach for AI-generated Image Detection [code]

  • Nan Zhong, Yiran Xu, Zhenxing Qian, Xinpeng Zhang. arXiv.

• Raising the Bar of AI-generated Image Detection with CLIP [page]

  • Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nießner, Luisa Verdoliva. arXiv.

• Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

  • Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano, Luisa Verdoliva. CVPR Workshop, 2023.

• Benchmarking Deepart Detection

  • Yabin Wang, Zhiwu Huang, Xiaopeng Hong. arXiv.

• CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images

  • Jordan J. Bird, Ahmad Lotf. arXiv.

• Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now [page]

  • Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra, Svetlana Lazebnik, D.A. Forsyth, Anand Bhattad. arXiv.

• On The Detection of Synthetic Images Generated by Diffusion Models [code]

  • Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Giovanni Poggi, Koki Nagano, Luisa Verdoliva. ICASSP, 2023.

• De-fake: Detection and attribution of fake images generated by text-to-image generation models

  • Zeyang Sha, Zheng Li, Ning Yu, Yang Zhang. CCS, 2023.

• Exposing fake images generated by text-to-image diffusion models

  Qiang Xu, Hao Wang, Laijin Meng, Zhongjie Mi, Jianye Yuan, Hong Yan. PRL, 2023.

• Towards Universal Fake Image Detectors that Generalize Across Generative Models [code]

  • Utkarsh Ojha, Yuheng Li, Yong Jae Lee. CVPR, 2023.

• DIRE for Diffusion-Generated Image Detection [code]

  • Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, Houqiang Li. ICCV, 2023.

• GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection

  • Yan Ju, Shan Jia, Jialing Cai, Haiying Guan, Siwei Lyu. IEEE Transactions on Multimedia, 2023.

• Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection [code]

  • Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, Yunchao Wei. CVPR, 2023.

2022

• Improving GAN-generated image detection generalization using unsupervised domain adaptation

  • Mingxu Zhang, Hongxia Wang, Peisong He, Asad Malik, and Hanqing Liu. ICME, 2022.

• Fusing Global and Local Features for Generalized AI-Synthesized Image Detection [code]

  • Yan Ju, Shan Jia, Lipeng Ke, Hongfei Xue, Koki Nagano, Siwei Lyu. ICIP, 2022.

• Detecting Generated Images by Real Images [code]

  • Bo Liu, Fan Yang, Xiuli Bi, Bin Xiao, Weisheng Li, Xinbo Gao. ECCV, 2022.

• FingerprintNet: Synthesized Fingerprints for Generated Image Detection

• Yonghyun Jeong, Doyeon Kim, Youngmin Ro, Pyounggeon Kim, and Jongwon Choi. ECCV, 2022.

2021

• Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
• D. Gragnaniello, D. Cozzolino, F. Marra, G. Poggi and L. Verdoliva. ICME, 2021.

2020

• What Makes Fake Images Detectable? Understanding Properties that Generalize [code]

  • Lucy Chai, David BauSer-Nam, and LimPhillip Isola. ECCV, 2020.

• Fourier Spectrum Discrepancies in Deep Network Generated Images [code]

  • Tarik Dzanic, Karan Shah, Freddie Witherden. NeurIPS, 2020.

• GAN-Generated Image Detection With Self-Attention Mechanism Against GAN Generator Defect

  • Zhongjie Mi, Xinghao Jiang, Tanfeng Sun, and Ke Xu. IEEE Journal of Selected Topics in Signal Processing, 2020.

• Leveraging Frequency Analysis for Deep Fake Image Recognition [code]

  • Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz. PMLR, 2020.

• Global Texture Enhancement for Fake Face Detection In the Wild [code]

  • Zhengzhe Liu, Xiaojuan Qi, Philip H.S. Torr. CVPR, 2020.

• CNN-generated images are surprisingly easy to spot...for now [code]

  • Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros. CVPR, 2020.

• Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions

• Ricard Durall, Margret Keuper, Janis Keuper. CVPR, 2020.

Please note that the collection focuses on papers published from 2020 onwards.