Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis

Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis

<p>Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. However, research has shown that the adversarial vulnerabilities of deep learning...

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Bibliographic Details
Main Author: Hassan Ali (3348749) (author)
Other Authors: Muhammad Suleman Khan (16494894) (author), Amer AlGhadhban (12287911) (author), Meshari Alazmi (452881) (author), Ahmad Alzamil (16494897) (author), Khaled Al-utaibi (16494900) (author), Junaid Qadir (16494902) (author)
Published: 2023
Subjects:
Information and computing sciences
Artificial intelligence
Machine learning
Machine learning security
Adversarial detection
Adversarial machine learning
Secure natural language processing
Adversarial signatures
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