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Title |
Adversarial Machine Learning: A Survey on the Influence Axis |
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Author |
Shahad Alzahrani, Taghreed Almalki, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 22 No. 5 pp. 193-203 |
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Abstract |
After the everyday use of systems and applications of artificial intelligence in our world. Consequently, machine learning technologies have become characterized by exceptional capabilities and unique and distinguished performance in many areas. However, these applications and systems are vulnerable to adversaries who can be a reason to confer the wrong classification by introducing distorted samples. Precisely, it has been perceived that adversarial examples designed throughout the training and test phases can include industrious Ruin the performance of the machine learning. This paper provides a comprehensive review of the recent research on adversarial machine learning. It's also worth noting that the paper only examines recent techniques that were released between 2018 and 2021. The diverse systems models have been investigated and discussed regarding the type of attacks, and some possible security suggestions for these attacks to highlight the risks of adversarial machine learning. |
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Keywords |
Machine Learning; Adversarial Machine Learning; Influence attack; Evasion attack; Data poisoning attack. |
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