137 citations · 266 across the 14 of their papers we have counts for
6 papers · 1 filter
WITCHcraft: Efficient PGD attacks with random step size
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum +4
State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points. Iterative FGSM-based methods wit…
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Ali Shafahi, Amin Ghiasi, Furong Huang +1
Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization. Th…
Adversarial attacks on Copyright Detection Systems
Parsa Saadatpanah, Ali Shafahi, Tom Goldstein
It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This pa…
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu, W. Ronny Huang, Ali Shafahi +4
Clean-label poisoning attacks inject innocuous looking (and "correctly" labeled) poison images into training data, causing a model to misclassify a targeted image after being train…
Adversarially robust transfer learning
Ali Shafahi, Parsa Saadatpanah, Chen Zhu +4
Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale train…
Adversarial Training for Free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi +6
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, t…