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The rapid advancement of deepfake technologies has sparked widespread public concern, particularly as face forgery poses a serious threat to public information security.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Face2face: Real-time face capture and reenactment of rgb videos
Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Large-scale celebfaces attributes (celeba) dataset
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Faceforensics++: Learning to detect manipulated facial images
Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Deferred neural rendering: Image synthesis using neural textures
Justus Thies, Michael Zollhöfer, and Matthias Nießner · 2019
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On the detection of digital face manipulation
Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, and Anil K Jain · 2020
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The deepfake detection challenge (dfdc) dataset
Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer · 2020
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Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection
Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy · 2020
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Celeb-df: A large-scale challenging dataset for deepfake forensics
Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu · 2020
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Thinking in frequency: Face forgery detection by mining frequency-aware clues
Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Grad-cam: visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2020
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Deepfakes and beyond: A survey of face manipulation and fake detection
Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Generalizing face forgery detection with high-frequency features
Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Exposing the deception: Uncovering more forgery clues for deepfake detection
Zhongjie Ba, Qingyu Liu, Zhenguang Liu, Shuang Wu, Feng Lin, Li Lu, and Kui Ren · 2024
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Deepfakes
FaceSwapDevs · 2024
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Contributing data to deepfake detection research
Google · 2024
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Can chatgpt detect deepfakes? a study of using multimodal large language models for media forensics
Shan Jia, Reilin Lyu, Kangran Zhao, Yize Chen, Zhiyuan Yan, Yan Ju, Chuanbo Hu, Xin Li, Baoyuan Wu, and Siwei Lyu · 2024
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Clipping the deception: Adapting vision-language models for universal deepfake detection
Sohail Ahmed Khan and Duc-Tien Dang-Nguyen · 2024
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Faceswap
Marek Kowalski · 2024
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Multi-attentional deepfake detection
Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu · 2021
Cited alongside, same era.
End-to-end reconstruction-classification learning for face forgery detection
Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, and Xiaokang Yang · 2022
Cited alongside, same era.
Dual contrastive learning for general face forgery detection
Ke Sun, Taiping Yao, Shen Chen, Shouhong Ding, Jilin Li, and Rongrong Ji · 2022
Cited alongside, same era.
Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection
Wanyi Zhuang, Qi Chu, Zhentao Tan, Qiankun Liu, Haojie Yuan, Changtao Miao, Zixiang Luo, and Nenghai Yu · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Sharegpt4v: Improving large multi-modal models with better captions
Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin · 2023
Cited alongside, same era.
Implicit identity leakage: The stumbling block to improving deepfake detection generalization
Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge · 2023
Cited alongside, same era.
Lisa: Reasoning segmentation via large language model
Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia · 2024
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Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao · 2024
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Preserving fairness generalization in deepfake detection
Li Lin, Xinan He, Yan Ju, Xin Wang, Feng Ding, and Shu Hu · 2024
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The global multimedia deepfake detection
INCLUSION·Conference on the Bund · 2024
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Yichen Shi, Yuhao Gao, Yingxin Lai, Hongyang Wang, Jun Feng, Lei He, Jun Wan, Changsheng Chen, Zitong Yu, and Xiaochun Cao · 2024
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Which face is real
Jevin West and Carl Bergstrom · 2024
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Analyzing fairness in deepfake detection with massively annotated databases
Ying Xu, Philipp Terhöst, Marius Pedersen, and Kiran Raja · 2024
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Common sense reasoning for deep fake detection
Yue Zhang, Ben Colman, Ali Shahriyari, and Gaurav Bharaj · 2024
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