REVIEW 2 cited by
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Neural networks are known to be vulnerable to adversarial examples. In this note, we evaluate the two white-box defenses that appeared at CVPR 2018 and find they are ineffective: when applying existing techniques, we can reduce the accuracy of the defended models to 0%.
Forward citations
Cited by 2 Pith papers
-
Stateful Detection of Black-Box Adversarial Attacks
The paper argues for stateful defenses over stateless ones to detect adversarial example generation via query history and introduces query blinding as a counter-attack.
-
Connecting Lyapunov Control Theory to Adversarial Attacks
Connects Lyapunov control theory to a provable defense against weaker adversarial attacks on neural networks.
Discussion (0). Sign in to comment.