WE4.L2.1
AFD: Defending Convolutional Neural Networks without Using Adversarial Samples
Nupur Thakur, Yuzhen Ding, Baoxin Li, Arizona State University, United States
Session:
WE4.L2: Information Forensics and Security 1 Spotlight
Track:
[IF] Information Forensics and Security
Location:
Spotlight Room 2
Presentation Time:
Wed, 17 Sep, 15:00 - 15:20 Anchorage Time (UTC -8)
Session Chair:
Touradj Ebrahimi, EPFL
Presentation
Discussion
Resources
No resources available.
Session WE4.L2
WE4.L2.1: AFD: Defending Convolutional Neural Networks without Using Adversarial Samples
Nupur Thakur, Yuzhen Ding, Baoxin Li, Arizona State University, United States
WE4.L2.2: ExDF: Explainable Deepfake Detection with Vision-Language Model
Shu-Tzu Lo, Academia Sinica, National Taiwan University of Science and Technology, Taiwan; Tai-Ming Huang, Yue-Hua Han, Academia Sinica, National Taiwan University, Taiwan; Kai-Lung Hua, National Taiwan University of Science and Technology, Microsoft, Taiwan; Jun-Cheng Chen, Academia Sinica, Taiwan
WE4.L2.3: ADVERSARIAL IMAGE PURIFICATION BY EXPLAINING ADVERSARIAL DETECTORS
Yi Ji, The University of Tokyo, Japan; Tuo Ji, Beijing University of Posts and Telecommunications, China; Hongliang Liu, Carnegie Mellon University, United States; Isao Echizen, National Institute of Informatics, Japan
Contacts