7 citations · 18 across the 4 of their papers we have counts for
5 papers
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca +2
Evading adversarial example detection defenses requires finding adversarial examples that must simultaneously (a) be misclassified by the model and (b) be detected as non-adversari…
Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning
Guneet S. Dhillon, Nicholas Carlini
Stochastic Activation Pruning (SAP) (Dhillon et al., 2018) is a defense to adversarial examples that was attacked and found to be broken by the "Obfuscated Gradients" paper (Athaly…
A Partial Break of the Honeypots Defense to Catch Adversarial Attacks
Nicholas Carlini
A recent defense proposes to inject "honeypots" into neural networks in order to detect adversarial attacks. We break the baseline version of this defense by reducing the detection…
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models
Nicholas Carlini
At IEEE S&P 2019, the paper "DeepSec: A Uniform Platform for Security Analysis of Deep Learning Model" aims to to "systematically evaluate the existing adversarial attack and defen…
Unrestricted Adversarial Examples
Tom B. Brown, Nicholas Carlini, Chiyuan Zhang +3
We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which stud…