Adversarial Attack Detection
Adversarial Attack Detection is a systematic method in imaging informatics security that identifies manipulated inputs designed to deceive machine learning models by distinguishing them from legitimate data through anomaly analysis in image-based model behavior.
837
Publications
49.7K
Citations
2.8K
Authors
782
Institutions
Publications per year
2017–2026
813
Parents
837
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, Yanjun Qi · 2018 · 1.9K citations · Full text
Adversarial attacks on medical machine learning
Samuel G. Finlayson, John D. Bowers, Joichi Ito et al. · Science · 2019 · 1K citations · Full text
1–5 of 837
2.8K
8
h-index
Indraprastha Institute of Information Technology Delhi, India
7 publications · 307 citations
7
h-index
Indraprastha Institute of Information Technology Delhi, India
7 publications · 307 citations
7
h-index
7
h-index
Indraprastha Institute of Information Technology Delhi, India
7 publications · 307 citations
7
h-index
1–5 of 2.8K
782
Tsinghua University
Beijing, China
18 h-index
78 publications
5K citations

Chinese Academy of Sciences
Beijing, China
17 h-index
57 publications
2.9K citations
Peng Cheng Laboratory
Shenzhen, China
15 h-index
32 publications
3K citations
Institute of Information Engineering
Beijing, China
14 h-index
48 publications
1.9K citations
University of Chinese Academy of Sciences
Beijing, China
14 h-index
45 publications
1.6K citations
Michigan State University
East Lansing, United States
13 h-index
41 publications
1.5K citations
Nanyang Technological University
Singapore, Singapore
12 h-index
23 publications
1.8K citations

Pennsylvania State University
State College, United States
12 h-index
32 publications
3K citations
1–8 of 782
Repository · 144 publications · 8K citations
40
h-index
28 publications · 1.2K citations
21
h-index
19 publications · 831 citations
17
h-index
Conference · 19 publications · 975 citations
17
h-index
14 publications · 505 citations
14
h-index
1–5