ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||5|
|Section||Computer Science and System Design, Application|
|Published online||23 June 2022|
Adversarial attack application analytics in machine learning
School of Computing, Wuhan Qingchuan University, Wuhan, Hubei, China
* Corresponding author: email@example.com
Machine learning is one of the most widely studied and applied technologies, but it is itself vulnerable to attack and its algorithms have the risk of privacy leakage. In this article, through the experts currently popular speech recognition scene, reveals how to build the antagonism against data, make its differences with the source data is subtle, so much so that humans can’t through sensory recognition, and machine learning model can accept and the classification of making the wrong decision, at the same time made attack, finally prospects the study model to research the development and application of security and privacy protection.
Key words: Machine learning / Privacy threats / Adversarial attacks
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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