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Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust.
Textural features for image classification
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Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez, and Fernando Alonso-Fernandez · 2018
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Stgan: A unified selective transfer network for arbitrary image attribute editing
Ming Liu, Yukang Ding, Min Xia, Xiao Liu, Errui Ding, Wangmeng Zuo, and Shilei Wen · 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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Mingxing Tan and Quoc V Le · 2019
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Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr · 2020
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Thinking in frequency: Face forgery detection by mining frequency-aware clues
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Ke Sun, Hong Liu, Qixiang Ye, Jianzhuang Liu, Yue Gao, Ling Shao, and Rongrong Ji · 2021
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Actionclip: A new paradigm for video action recognition
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Multi-attentional deepfake detection
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Learning self-consistency for deepfake detection
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Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection
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Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao · 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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Wilddeepfake: A challenging real-world dataset for deepfake detection
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Local relation learning for face forgery detection
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Detecting deepfakes with self-blended images
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Dual contrastive learning for general face forgery detection
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Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection
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