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Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?

Year: 2019

Type: preprint

Abstract: No.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1902.02322

Author: Carlini, Nicholas

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2913866791

Cites: 3

Cited by: 22

Related to: 20