8 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 8 cited
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Dequan Wang, An Ju, Evan Shelhamer +2
Adversarial attacks optimize against models to defeat defenses. Existing defenses are static, and stay the same once trained, even while attacks change. We argue that models should…
cs.CV2021★ 1 cited
Model-Agnostic Defense for Lane Detection against Adversarial Attack
Henry Xu, An Ju, David Wagner
Susceptibility of neural networks to adversarial attack prompts serious safety concerns for lane detection efforts, a domain where such models have been widely applied. Recent work…