Finding Black-box Adversarial Examples with Limited Queries Black-box attacks generate adversarial examples (AEs) against deep neural networks with only API access to the victim model.
Existing black-box attacks can be grouped into two main categories:
Transfer Attacks use white-box attacks on local models to find candidate adversarial examples that transfer to the target model.
Optimization Attacks use queries to the target model and apply optimization techniques to search for adversarial examples.
Five students from our group presented posters at the department’s
Anshuman Suri's Overview Talk
UVA Group Dinner at IEEE Security and Privacy 2018
Including our newest faculty member, Yongwhi Kwon, joining UVA in Fall 2018!
Yuan Tian, Fnu Suya, Mainuddin Jonas, Yongwhi Kwon, David Evans, Weihang Wang, Aihua Chen, Weilin Xu
## Poster Session
Fnu Suya (with Yuan Tian and David Evans), Adversaries Don’t Care About Averages: Batch Attacks on Black-Box Classifiers [PDF]
Mainuddin Jonas (with David Evans), Enhancing Adversarial Example Defenses Using Internal Layers [PDF]