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Adversarial Machine Learning in Text Processing: A Literature Survey
Izzat Alsmadi
, Nura Aljaafari
, Mahmoud Nazzal
, Shadan Alhamed
, Ahmad H. Sawalmeh
, Conrado P. Vizcarra
,
Abdallah Khreishah
, Muhammad Anan
, Abdulelah Algosaibi
, Mohammed Abdulaziz Al-Naeem
, Adel Aldalbahi
, Abdulaziz Al-Humam
Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
31
Scopus citations
Overview
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Keyphrases
Machine Learning Algorithms
100%
Text Processing
100%
Training Model
100%
Adversarial Learning
100%
Text Generation
100%
Text Generation Model
66%
Computer-generated Text
66%
Machine Learning
33%
New Memory
33%
Attacker
33%
Human-machine Systems
33%
Real-time Constraints
33%
Text-to-image
33%
Human Judgment
33%
Utilization Rate
33%
Processing Applications
33%
Writing Style
33%
Memory Consumption
33%
Memory Models
33%
Transformer
33%
Increased Performance
33%
Increasing Trends
33%
Targeted Methods
33%
Major Subject
33%
Generation Algorithm
33%
Assessment Metrics
33%
Adversarial Model
33%
White-box Attack
33%
Latent Representation
33%
Black Hole Attack
33%
Targeted Adversarial Attack
33%
Defense Model
33%
Sentence Embedding Models
33%
Attack Generation
33%
Conditional GAN
33%
Semi-automated Assessment
33%
Adversarial Text
33%
Targeted Attack
33%
Computer Science
Adversarial Machine Learning
100%
Text Processing
100%
Machine Learning Algorithm
75%
Generation Model
50%
Research Effort
25%
Machine Learning
25%
Attackers
25%
Information System
25%
Systems and Application
25%
Time Constraint
25%
Memory Model
25%
Memory Utilization
25%
Adversarial Model
25%
Targeted Attack
25%
Chemical Engineering
Learning System
100%