Leveraging imperfect restoration for data availability attack

• Main Authors: Huang, Yi, Styborski, Jeremy, Lyu, Mingzhi, Wang, Fan, Kong, Adams Wai Kin
• Other Authors: Interdisciplinary Graduate School (IGS)
• Format: Conference or Workshop Item
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Data availability attacks; Supervised learning; Self-supervised learning
• Online Access: https://hdl.handle.net/10356/179131; https://eccv.ecva.net/virtual/2024/poster/1216
• Institution: Nanyang Technological University

The abundance of online data is at risk of unauthorized usage in training deep learning models. To counter this, various Data Availability Attacks (DAAs) have been devised to make data unlearnable for such models by subtly perturbing the training data. However, existing attacks often excel against e...