{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T18:18:20Z","timestamp":1774376300322,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":83,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U.S. National Science Foundation","award":["IIS-2434967"],"award-info":[{"award-number":["IIS-2434967"]}]},{"name":"National Artificial Intelligence Research Resource"},{"name":"TACC Lonestar6"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3755244","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T07:26:38Z","timestamp":1761377198000},"page":"11424-11433","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Rethinking Individual Fairness in Deepfake Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9511-357X","authenticated-orcid":false,"given":"Aryana","family":"Hou","sequence":"first","affiliation":[{"name":"Clarkstown High School South, West Nyack, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2583-7651","authenticated-orcid":false,"given":"Li","family":"Lin","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6591-2076","authenticated-orcid":false,"given":"Justin","family":"Li","sequence":"additional","affiliation":[{"name":"Carmel High School, Carmel, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1446-4140","authenticated-orcid":false,"given":"Shu","family":"Hu","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Dolhansky Brian et al. 2020. The DeepFake Detection Challenge Dataset. arXiv:2006.07397"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00408"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning. PMLR, 7621-7639","author":"Chen Baoying","year":"2024","unstructured":"Baoying Chen, Jishen Zeng, Jianquan Yang, and Rui Yang. 2024c. DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images. In Proceedings of the 41st International Conference on Machine Learning. PMLR, 7621-7639."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3218"},{"key":"e_1_3_2_1_5_1","volume-title":"Masked conditional diffusion model for enhancing deepfake detection. arXiv preprint arXiv:2402.00541","author":"Chen Tiewen","year":"2024","unstructured":"Tiewen Chen, Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, and Xin Wang. 2024a. Masked conditional diffusion model for enhancing deepfake detection. arXiv preprint arXiv:2402.00541 (2024)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00114"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00104"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095167"},{"key":"e_1_3_2_1_10_1","volume-title":"Raising the Bar of AI-generated Image Detection with CLIP. arXiv preprint arXiv:2312.00195","author":"Cozzolino Davide","year":"2023","unstructured":"Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nie\u00dfner, and Luisa Verdoliva. 2023. Raising the Bar of AI-generated Image Detection with CLIP. arXiv preprint arXiv:2312.00195 (2023)."},{"key":"e_1_3_2_1_11_1","volume-title":"Diffusion models beat gans on image synthesis. Advances in neural information processing systems","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, Vol. 34 (2021), 8780-8794."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Learning Representations.","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_14_1","volume-title":"Fourier spectrum discrepancies in deep network generated images. Advances in neural information processing systems","author":"Dzanic Tarik","year":"2020","unstructured":"Tarik Dzanic, Karan Shah, and Freddie Witherden. 2020. Fourier spectrum discrepancies in deep network generated images. Advances in neural information processing systems, Vol. 33 (2020), 3022-3032."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR59079.2023.00035"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462621"},{"key":"e_1_3_2_1_17_1","volume-title":"Sharpness-aware Minimization for Efficiently Improving Generalization. In International Conference on Learning Representations.","author":"Foret Pierre","year":"2020","unstructured":"Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. 2020. Sharpness-aware Minimization for Efficiently Improving Generalization. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_18_1","volume-title":"International conference on machine learning. PMLR, 3247-3258","author":"Frank Joel","year":"2020","unstructured":"Joel Frank, Thorsten Eisenhofer, Lea Sch\u00f6nherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. 2020. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning. PMLR, 3247-3258."},{"key":"e_1_3_2_1_19_1","unstructured":"Google and Jigsaw. 2019. Deepfakes dataset by Google & Jigsaw. https:\/\/ai.googleblog.com\/2019\/09\/contributing-data-to-deepfakedetection.html."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-66431-1_43"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746597"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR54900.2022.00047"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3157297"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3155-3165","author":"Xiao","unstructured":"Xiao Guo et al., 2023. Hierarchical fine-grained image forgery detection and localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3155-3165."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBIOM.2021.3132237"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01669"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01669"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Ashish Hooda et al. 2024. D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles. WACV (2024).","DOI":"10.1109\/WACV57701.2024.00377"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414582"},{"key":"e_1_3_2_1_31_1","volume-title":"Local Statistics for Generative Image Detection. arXiv e-prints","author":"Wong Yung Jer","year":"2023","unstructured":"Yung Jer Wong and Teck Khim Ng. 2023. Local Statistics for Generative Image Detection. arXiv e-prints (2023), arXiv-2310."},{"key":"e_1_3_2_1_32_1","volume-title":"GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection","author":"Yan Ju","year":"2023","unstructured":"Yan Ju et al., 2023. GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection. IEEE Transactions on Multimedia (2023)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00459"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20675"},{"key":"e_1_3_2_1_36_1","volume-title":"Robust AI-Generated Face Detection with Imbalanced Data. MIPR","author":"Krubha Yamini Sri","year":"2025","unstructured":"Yamini Sri Krubha, Aryana Hou, Braden Vester, Web Walker, Xin Wang, Li Lin, and Shu Hu. 2025. Robust AI-Generated Face Detection with Imbalanced Data. MIPR (2025)."},{"key":"e_1_3_2_1_37_1","unstructured":"Hanzhe Li Jiaran Zhou Yuezun Li Baoyuan Wu Bin Li and Junyu Dong. 2024. FreqBlender: Enhancing DeepFake detection by blending frequency knowledge. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"e_1_3_2_1_39_1","first-page":"7","article-title":"Celeb-DF","volume":"6","author":"Li Yuezun","year":"2020","unstructured":"Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. 2020. Celeb-DF: A New Dataset for Deepfake Forensics. In CVPR. 6,7.","journal-title":"A New Dataset for Deepfake Forensics. In CVPR."},{"key":"e_1_3_2_1_40_1","unstructured":"Li Lin Irene Amerini Xin Wang Shu Hu et al. 2024a. Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images. MIPR (2024)."},{"key":"e_1_3_2_1_41_1","volume-title":"2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 1-7.","author":"Lin Li","year":"2024","unstructured":"Li Lin, Irene Amerini, Xin Wang, Shu Hu, et al., 2024b. Robust CLIP-based detector for exposing diffusion model-generated images. In 2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 1-7."},{"key":"e_1_3_2_1_42_1","volume-title":"Detecting Multimedia Generated by Large AI Models: A Survey. arXiv preprint arXiv:2402.00045","author":"Lin Li","year":"2024","unstructured":"Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, and Shu Hu. 2024c. Detecting Multimedia Generated by Large AI Models: A Survey. arXiv preprint arXiv:2402.00045 (2024)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01591"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Li Lin Xin Wang Shu Hu et al. 2024 e. AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark. arXiv preprint arXiv:2406.00783 (2024).","DOI":"10.1109\/CVPR52734.2025.00332"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01024"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"e_1_3_2_1_48_1","volume-title":"Exposing the Fake: Effective Diffusion-Generated Images Detection. In The Second Workshop on New Frontiers in Adversarial Machine Learning.","unstructured":"RuiPeng Ma et al., 2023. Exposing the Fake: Effective Diffusion-Generated Images Detection. In The Second Workshop on New Frontiers in Adversarial Machine Learning."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACVW.2019.00020"},{"key":"e_1_3_2_1_50_1","volume-title":"International Conference on Machine Learning. PMLR, 7097-7107","author":"Mukherjee Debarghya","year":"2020","unstructured":"Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun. 2020. Two simple ways to learn individual fairness metrics from data. In International Conference on Machine Learning. PMLR, 7097-7107."},{"key":"e_1_3_2_1_51_1","volume-title":"GBDF: gender balanced deepfake dataset towards fair deepfake detection. arXiv preprint arXiv:2207.10246","author":"Nadimpalli Aakash Varma","year":"2022","unstructured":"Aakash Varma Nadimpalli and Ajita Rattani. 2022. GBDF: gender balanced deepfake dataset towards fair deepfake detection. arXiv preprint arXiv:2207.10246 (2022)."},{"key":"e_1_3_2_1_52_1","volume-title":"Thiagarajan","author":"Narayanaswamy Vivek","year":"2024","unstructured":"Vivek Narayanaswamy, Kowshik Thopalli, Rushil Anirudh, Yamen Mubarka, Wesam Sakla, and Jayaraman J. Thiagarajan. 2024. On the Use of Anchoring for Training Vision Models. In Advances in Neural Information Processing Systems, A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (Eds.), Vol. 37. Curran Associates, Inc., 95438-95455."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00011"},{"key":"e_1_3_2_1_54_1","volume-title":"Nyee Thoang Lim, Chun Yong Chong, and Mei Kuan Lim.","author":"Pu Muxin","year":"2022","unstructured":"Muxin Pu, Meng Yi Kuan, Nyee Thoang Lim, Chun Yong Chong, and Mei Kuan Lim. 2022b. Fairness Evaluation in Deepfake Detection Models using Metamorphic Testing. arXiv preprint arXiv:2203.06825 (2022)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108832"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"e_1_3_2_1_57_1","volume-title":"Improving Generalization for AI-Synthesized Voice Detection. AAAI","author":"Ren Hainan","year":"2024","unstructured":"Hainan Ren, Lin Li, Chun-Hao Liu, Xin Wang, and Shu Hu. 2024. Improving Generalization for AI-Synthesized Voice Detection. AAAI (2024)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00009"},{"key":"e_1_3_2_1_59_1","volume-title":"Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 3418-3432","author":"Zeyang","unstructured":"Zeyang Sha et al., 2023. De-fake: Detection and attribution of fake images generated by text-to-image generation models. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 3418-3432."},{"key":"e_1_3_2_1_60_1","volume-title":"Average individual fairness: Algorithms, generalization and experiments. Advances in neural information processing systems","author":"Sharifi-Malvajerdi Saeed","year":"2019","unstructured":"Saeed Sharifi-Malvajerdi, Michael Kearns, and Aaron Roth. 2019. Average individual fairness: Algorithms, generalization and experiments. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00402"},{"key":"e_1_3_2_1_62_1","volume-title":"International conference on machine learning. PMLR, 6105-6114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. PMLR, 6105-6114."},{"key":"e_1_3_2_1_63_1","volume-title":"An examination of fairness of AI models for deepfake detection. IJCAI","author":"Trinh Loc","year":"2021","unstructured":"Loc Trinh and Yan Liu. 2021. An examination of fairness of AI models for deepfake detection. IJCAI (2021)."},{"key":"e_1_3_2_1_64_1","volume-title":"NVAE: A deep hierarchical variational autoencoder. Advances in neural information processing systems","author":"Vahdat Arash","year":"2020","unstructured":"Arash Vahdat and Jan Kautz. 2020. NVAE: A deep hierarchical variational autoencoder. Advances in neural information processing systems, Vol. 33 (2020), 19667-19679."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA230558"},{"key":"e_1_3_2_1_67_1","volume-title":"Li Lin, Hui Guo, Shu Hu, Ming-Ching Chang, Pradeep K Atrey, and Siwei Lyu.","author":"Wang Xin","year":"2024","unstructured":"Xin Wang, Ting Yu Tsai, Li Lin, Hui Guo, Shu Hu, Ming-Ching Chang, Pradeep K Atrey, and Siwei Lyu. 2024. Spotting the Fakes: A Deep Dive into GAN-Generated Face Detection. ACM Transactions on Multimedia Computing, Communications and Applications (2024)."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02051"},{"key":"e_1_3_2_1_69_1","volume-title":"The emergence of deepfake technology: A review. Technology innovation management review","author":"Westerlund Mika","year":"2019","unstructured":"Mika Westerlund. 2019. The emergence of deepfake technology: A review. Technology innovation management review, Vol. 9, 11 (2019)."},{"key":"e_1_3_2_1_70_1","volume-title":"Preserving AUC Fairness in Learning with Noisy Protected Groups. In The 42nd International Conference on Machine Learning (ICML).","author":"Wu Mingyang","year":"2025","unstructured":"Mingyang Wu, Li Lin, Wenbin Zhang, Xin Wang, Zhenhuan Yang, and Shu Hu. 2025. Preserving AUC Fairness in Learning with Noisy Protected Groups. In The 42nd International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_71_1","unstructured":"Qiang Xu et al. 2023. Exposing fake images generated by text-to-image diffusion models. Pattern Recognition Letters (2023)."},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2024.3365421"},{"key":"e_1_3_2_1_73_1","volume-title":"International Conference on Learning Representations.","author":"Xu Zhipei","year":"2025","unstructured":"Zhipei Xu, Xuanyu Zhang, Runyi Li, Zecheng Tang, Qing Huang, and Jian Zhang. 2025. FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00858"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02048"},{"key":"e_1_3_2_1_76_1","volume-title":"Deepfakebench: A comprehensive benchmark of deepfake detection. In NeurIPS.","author":"Yan Zhiyuan","year":"2023","unstructured":"Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, and Baoyuan Wu. 2023b. Deepfakebench: A comprehensive benchmark of deepfake detection. In NeurIPS."},{"key":"e_1_3_2_1_77_1","volume-title":"CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition. arXiv preprint arXiv:2310.00359","author":"Yang Shanmin","year":"2023","unstructured":"Shanmin Yang, Hui Guo, Shu Hu, Bin Zhu, Ying Fu, Siwei Lyu, Xi Wu, and Xin Wang. 2023. CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition. arXiv preprint arXiv:2310.00359 (2023)."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3335203.3335724"},{"key":"e_1_3_2_1_79_1","volume-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","author":"Zhang Kai","year":"2017","unstructured":"Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, Vol. 26, 7 (2017), 3142-3155."},{"key":"e_1_3_2_1_80_1","volume-title":"X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection. In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 1-8.","author":"Zhang Lei","year":"2024","unstructured":"Lei Zhang, Hao Chen, Shu Hu, Bin Zhu, Ching-Sheng Lin, Xi Wu, Jinrong Hu, and Xin Wang. 2024. X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection. In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 1-8."},{"key":"e_1_3_2_1_81_1","volume-title":"X-transfer: A transfer learning-based framework for robust gan-generated fake image detection. arXiv preprint arXiv:2310.04639","author":"Zhang Lei","year":"2023","unstructured":"Lei Zhang, Hao Chen, Shu Hu, Bin Zhu, Xi Wu, Jinrong Hu, and Xin Wang. 2023. X-transfer: A transfer learning-based framework for robust gan-generated fake image detection. arXiv preprint arXiv:2310.04639, Vol. 2 (2023)."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"e_1_3_2_1_83_1","first-page":"59570","article-title":"Breaking Semantic Artifacts for Generalized AI-generated Image Detection","volume":"37","author":"Zheng Chende","year":"2024","unstructured":"Chende Zheng, Chenhao Lin, Zhengyu Zhao, Hang Wang, Xu Guo, Shuai Liu, and Chao Shen. 2024. Breaking Semantic Artifacts for Generalized AI-generated Image Detection. Advances in Neural Information Processing Systems, Vol. 37 (2024), 59570-59596.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","location":"Dublin Ireland","acronym":"MM '25","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3746027.3755244","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755244","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T19:47:17Z","timestamp":1765309637000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3755244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":83,"alternative-id":["10.1145\/3746027.3755244","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3755244","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}