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

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 networks...

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Bibliographic Details
Main Author: Hassan, Ali (author)
Other Authors: Khan, Muhammad Suleman (author), AlGhadhban, Amer (author), Alazmi, Meshari (author), Alzamil, Ahmed (author), Al-utaibi, Khaled (author), Qadir, Junaid (author)
Format: article
Published: 2023
Subjects:
Machine learning security
Adversarial detection
Adversarial machine learning
Secure natural language processing
Adversarial signatures
Online Access:http://dx.doi.org/10.1016/j.cose.2023.103367
https://www.sciencedirect.com/science/article/pii/S0167404823002778
http://hdl.handle.net/10576/65987
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