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Anomaly-Detection Defense Against Test-Time Evasion Attacks on Robust DNNs
Ye Tao
, Zhen Xiang
,
David J. Miller
,
George Kesidis
Electrical Engineering
Computer Science and Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
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Chapter
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Dive into the research topics of 'Anomaly-Detection Defense Against Test-Time Evasion Attacks on Robust DNNs'. Together they form a unique fingerprint.
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Keyphrases
Anomaly Detection
100%
Deep Neural Network
100%
Test-time Evasion Attack
100%
Robust Classification
40%
Classification Accuracy
20%
Evade
20%
Attacker
20%
Classification Problem
20%
Dynamic Data Driven Applications Systems (DDDAS)
20%
Clean Data
20%
System Framework
20%
Attack Strategy
20%
Critical Infrastructure
20%
Detection Problem
20%
Defense Method
20%
Problem Recognition
20%
High-dimensional Analysis
20%
Classification Recognition
20%
Network Support
20%
Address Detection
20%
Large Perturbation
20%
Robust Classifier
20%
Defense Capability
20%
Attack Defense
20%
Classification Detection
20%
Robust Training
20%
Anomaly Detector
20%
Attack Strength
20%
Attack Success Rate
20%
Biochemistry, Genetics and Molecular Biology
Cooperation
100%