14 citations · 20 across the 3 of their papers we have counts for
3 papers
cs.CV2021★ 2 cited
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Yixu Wang, Jie Li, Hong Liu +4
Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we obse…
cs.CV2020★ 4 cited
Projection & Probability-Driven Black-Box Attack
Jie Li, Rongrong Ji, Hong Liu +4
Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer f…
cs.CV2018★ 14 cited
Universal Perturbation Attack Against Image Retrieval
Jie Li, Rongrong Ji, Hong Liu +3
Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data…