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Deepfakes are AI-generated media in which an image or video has been digitally modified.
“Use of a capsule network to detect fake images and videos. arxiv 2019,”
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“Joint face detection and facial expression recognition with mtcnn,”
Jia Xiang and Gengming Zhu, · 2017
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“Faceforensics: A large-scale video dataset for forgery detection in human faces,”
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“Voxceleb2: Deep speaker recognition,”
Joon Son Chung, Arsha Nagrani, and Andrew Zisserman, · 2018
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“Mesonet: a compact facial video forgery detection network,”
Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen, · 2018
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“Efficientnet: Rethinking model scaling for convolutional neural networks,”
Mingxing Tan and Quoc Le, · 2019
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“The deepfake detection challenge (dfdc) dataset,” 2020
Brian Dolhansky, Joanna Bitton, et al., · 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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“Multi-attentional deepfake detection,”
Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu, · 2021
Cited alongside, same era.
“Deepfakes detection methods: A literature survey,”
MC Weerawardana and TGI Fernando, · 2021
Cited alongside, same era.
“Fakeavceleb: A novel audio-video multimodal deepfake dataset,”
Hasam Khalid, Shahroz Tariq, et al., · 2021
Cited alongside, same era.
“Evaluation of an audio-video multimodal deepfake dataset using unimodal and multimodal detectors,”
Hasam Khalid, Minha Kim, et al., · 2021
Cited alongside, same era.
“Swin transformer: Hierarchical vision transformer using shifted windows,”
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo, · 2021
Cited alongside, same era.
“Kodf: A large-scale korean deepfake detection dataset,”
“Trusted media challenge dataset and user study,”
Weiling Chen, Sheng Lun Benjamin Chua, Stefan Winkler, and See-Kiong Ng, · 2022
Later among the works it cites.
“Multimodal forgery detection using ensemble learning,”
Ammarah Hashmi, Sahibzada Adil Shahzad, et al., · 2022
Later among the works it cites.
“Voice-face homogeneity tells deepfake,”
Harry Cheng, Yangyang Guo, et al., · 2022
Later among the works it cites.
“Understanding the mel spectrogram,” Aug 2022
Leland Roberts, · 2022
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“Lip sync matters: A novel multimodal forgery detector,”
Sahibzada Adil Shahzad, Ammarah Hashmi, et al., · 2022
Later among the works it cites.
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Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae, · 2021
Cited alongside, same era.
“Exploring temporal coherence for more general video face forgery detection,”
Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, and Fang Wen, · 2021
Cited alongside, same era.
“Deepfake detection: A systematic literature review,”
Md Shohel Rana, Mohammad Nur Nobi, Beddhu Murali, and Andrew H Sung, · 2022
Cited alongside, same era.
“Model attribution of face-swap deepfake videos,”
Shan Jia, Xin Li, and Siwei Lyu, · 2022
Cited alongside, same era.
“Faketracer: Exposing deepfakes with training data contamination,”
Pu Sun, Yuezun Li, Honggang Qi, and Siwei Lyu, · 2022
Cited alongside, same era.
Luca Guarnera, Oliver Giudice, and Sebastiano Battiato, · 2023
Closest in time.
“Audio-visual person-of-interest deepfake detection,”
Davide Cozzolino, Alessandro Pianese, Matthias Nießner, and Luisa Verdoliva, · 2023
Closest in time.
“Avoid-df: Audio-visual joint learning for detecting deepfake,”
Wenyuan Yang, Xiaoyu Zhou, Zhikai Chen, et al., · 2023
Closest in time.
“Self-supervised video forensics by audio-visual anomaly detection,”
Chao Feng, Ziyang Chen, and Andrew Owens, · 2023
Closest in time.
“Ai-synthesized voice detection using neural vocoder artifacts,”
Chengzhe Sun, Shan Jia, Shuwei Hou, and Siwei Lyu, · 2023
Closest in time.