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Server-based manipulation attacks against machine learning models
Cong Liao
,
Sencun Zhu
, Haoti Zhong
,
Anna Squicciarini
Computer Science and Engineering
College of Information Sciences and Technology
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
9
Link opens in a new tab
Scopus citations
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Dive into the research topics of 'Server-based manipulation attacks against machine learning models'. Together they form a unique fingerprint.
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Computer Science
Learning System
100%
Machine Learning Model
100%
Experimental Result
50%
Attackers
50%
Supervised Learning
50%
Logistic Regression
50%
Data Analytics
50%
Machine Learning Approach
50%
Gradient Descent
50%
Machine Learning Algorithm
50%
Image Classification
50%
Malicious Attack
50%
Deep Learning Model
50%
Machine Learning
50%
Deep Learning Method
50%
Cloud Computing
50%
Convolutional Neural Network
50%
spam filter
50%
Introduce Bias
50%
Keyphrases
Machine Learning Models
100%
Manipulation Attack
100%
Server-side
66%
Image Classification
33%
Machine Learning Algorithms
33%
Attacker
33%
Logistic Regression
33%
Data Analytics
33%
Machine Learning Approach
33%
Misclassification
33%
Malicious Attacks
33%
Cloud Services
33%
Enron
33%
Convolutional Neural Network
33%
Gradient Descent
33%
Deep Learning Model
33%
Deep Learning System
33%
Deep Machine Learning
33%
Regression Neural Network
33%
Machine Learning Learning
33%
Filtered Images
33%
Cloud Adoption
33%
Various Applications
33%
Image Dataset
33%
Evade
33%