[SS2-01] Deepfake Generation and Detection – An Overview（ディープフェイクの生成と検出）
略歴： Huy H. Nguyen received B.S. degree in Information Technology from VNUHCM - University of Science, Ho Chi Minh City, Vietnam in 2013. He is currently pursuing a Ph.D. degree in computer science at the Graduate University for Advanced Studies (SOKENDAI) in connection with the National Institute of Informatics, Japan. His current research interests include deepfake detection, biometrics, and machine learning.
概要： Advances in machine learning and their interference with computer graphics allow us to easily generate high-quality images and videos. State-of-the-art manipulation methods enable the real-time manipulation of videos obtained from social networks. It is also possible to generate videos from a single portrait image. By combining these methods with speech synthesis, attackers can create a realistic video of some person saying something that they never said and distribute it on the internet. This results in loosing social trust, making confusion, and harming people’s reputation. Several countermeasures have been proposed to tackle this problem, from using hand-crafted features to using convolutional neural network. Some countermeasures use images as input and other leverage temporal information in videos. Their output could be binary (bona fide or fake) or muti-class (deepfake detection), or segmentation masks (manipulation localization). Since deepfake methods evolve rapidly, dealing with unseen ones is still a challenging problem. Some solutions have been proposed, however, this problem is not completely solved. In this talk, I will provide an overview on both deepfake generation and deepfake detection/localization. I will mainly focus on image and video domain and also introduce some audiovisual-based methods on both sides. Some open discussions and future directions are also included.
概要： Advances in machine learning and their interference with computer graphics allow us to easily generate high-quality images and videos. State-of-the-art manipulation methods enable the real-time manipulation of videos obtained from social networks. It is also possible to generate videos from a single portrait image. By combining these methods with speech synthesis, attackers can create a realistic video of some person saying something that they never said and distribute it on the internet. This results in loosing social trust, making confusion, and harming people’s reputation. Several countermeasures have been proposed to tackle this problem, from using hand-crafted features to using convolutional neural network. Some countermeasures use images as input and other leverage temporal information in videos. Their output could be binary (bona fide or fake) or muti-class (deepfake detection), or segmentation masks (manipulation localization). Since deepfake methods evolve rapidly, dealing with unseen ones is still a challenging problem. Some solutions have been proposed, however, this problem is not completely solved. In this talk, I will provide an overview on both deepfake generation and deepfake detection/localization. I will mainly focus on image and video domain and also introduce some audiovisual-based methods on both sides. Some open discussions and future directions are also included.
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