├── assets ├── test.txt └── cover_update.jpg ├── LICENSE └── README.md /assets/test.txt: -------------------------------------------------------------------------------- 1 | tst 2 | -------------------------------------------------------------------------------- /assets/cover_update.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Daisy-Zhang/Awesome-AIGC-Detection/HEAD/assets/cover_update.jpg -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Daichi Zhang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-AIGC-Detection![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) 2 | 3 | ![from internet](assets/cover_update.jpg) 4 | 5 | The rapid development of AIGC(AI-Generated Content) has significantly influenced our daily life and how to detect them become a crucial challenge for AI safety now. This is a collection list of AIGC Detection related research that aims to facilitate the development of related fields. 6 | 7 | If you find this list helpful in your research, your star and recommendation will be my pleasure. If you want to contribute to this list, welcome to send me a pull request or contact me :) 8 | 9 | If you are also interested in Deepfakes Detection, please refer to: [Awesome Deepfakes Detection](https://github.com/Daisy-Zhang/Awesome-Deepfakes-Detection). 10 | 11 | ## Research Papers 12 | 13 | * Zero-Shot Detection of AI-Generated Images, ECCV 2024: [Paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02665.pdf) [Code](https://github.com/grip-unina/ZED/) 14 | 15 | * LaRE2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection, CVPR 2024: [Paper](https://arxiv.org/abs/2403.17465) 16 | 17 | * How to Trace Latent Generative Model Generated Images without Artificial Watermark? ICML 2024: [Paper](https://arxiv.org/abs/2405.13360) 18 | 19 | * DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images, ICML 2024: [Paper](https://icml.cc/virtual/2024/poster/33086) 20 | 21 | * Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection, CVPR 2024: [Paper](https://arxiv.org/abs/2312.10461) [Code](https://github.com/chuangchuangtan/NPR-DeepfakeDetection) 22 | 23 | * Mastering Deepfake Detection: A Cuting-Edge Approach to Distinguish GAN and Difusion-Model Images, ACM Transactions on Multimedia Computing, Communications and Applications 2024: [Paper](https://dl.acm.org/doi/pdf/10.1145/3652027) 24 | 25 | * Robust Image Watermarking using Stable Diffusion, arXiv 2024: [Paper](https://arxiv.org/abs/2401.04247) 26 | 27 | * Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? arXiv 2024: [Paper](https://arxiv.org/abs/2402.03214) 28 | 29 | * GenDet: Towards Good Generalizations for AI-Generated Image Detection, arXiv 2024: [Paper](https://arxiv.org/abs/2312.08880) 30 | 31 | * FakeBench: Uncover the Achilles’ Heels of Fake Images with Large Multimodal Models, arXiv 2024: [Paper](https://arxiv.org/abs/2404.13306) 32 | 33 | * A Single Simple Patch is All You Need for AI-generated Image Detection, arXiv 2024: [Paper](https://arxiv.org/abs/2402.01123) 34 | 35 | * WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection, arXiv 2024: [Paper](https://arxiv.org/abs/2402.11843) 36 | 37 | * DIRE for Diffusion-Generated Image Detection, ICCV 2023: [Paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_DIRE_for_Diffusion-Generated_Image_Detection_ICCV_2023_paper.pdf) [Code](https://github.com/ZhendongWang6/DIRE) 38 | 39 | * Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images, NeurIPS 2023: [Paper](https://proceedings.neurips.cc/paper_files/paper/2023/file/505df5ea30f630661074145149274af0-Paper-Datasets_and_Benchmarks.pdf) [Code](https://github.com/Inf-imagine/Sentry) 40 | 41 | * Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection, CVPR 2023: [Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Learning_on_Gradients_Generalized_Artifacts_Representation_for_GAN-Generated_Images_Detection_CVPR_2023_paper.pdf) 42 | 43 | * Towards Universal Fake Image Detectors that Generalize Across Generative Models, CVPR 2023: [Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Ojha_Towards_Universal_Fake_Image_Detectors_That_Generalize_Across_Generative_Models_CVPR_2023_paper.pdf) [Code](https://github.com/WisconsinAIVision/UniversalFakeDetect) 44 | 45 | * Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality, ICCV workshop 2023: [Paper](https://openaccess.thecvf.com/content/ICCV2023W/DFAD/papers/Lorenz_Detecting_Images_Generated_by_Deep_Diffusion_Models_Using_Their_Local_ICCVW_2023_paper.pdf) 46 | 47 | * Exposing the Fake: Effective Diffusion-Generated Images Detection, arXiv 2023: [Paper](https://arxiv.org/abs/2307.06272) 48 | 49 | * GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image, arXiv 2023: [Paper](https://arxiv.org/abs/2306.08571) [Code](https://github.com/GenImage-Dataset/GenImage) 50 | 51 | * PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection, arXiv 2023: [Paper](https://arxiv.org/abs/2311.12397) 52 | 53 | * CNN-generated images are surprisingly easy to spot...for now, CVPR 2020: [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_CNN-Generated_Images_Are_Surprisingly_Easy_to_Spot..._for_Now_CVPR_2020_paper.pdf) [Code](https://github.com/peterwang512/CNNDetection) 54 | --------------------------------------------------------------------------------