 Learning Systems Under Attack—Adversarial Attacks, Defenses and BeyondDanilo Vasconcellos Vargas ()
A chapter in Autonomous Vehicles, 2021, pp 147-161 from Springer Abstract: Abstract Deep learning has brought many advances to various fields and enabled applications such as speech and visual recognition to flourish. However, recent findings show that Deep Neural NetworksDeep neural network (DNN) still have many problems of their own. The many vulnerabilities present in DNNs unable their application to critical problems. Here, some of these vulnerabilities will be reviewed and many of their possible solutions will be discussed. Regarding legislation, a series of practices will be discussed that could allow for legislation to deal with the increasingly different algorithms available. A small overhead for a safer society. Lastly, as artificial intelligenceArtificial intelligence advances, algorithms should get closer to human beings and legislation itself should face deep philosophical questions in an age in which we will be challenged to reinvent ourselves, as a society and beyond. Keywords: Adversarial machine learning; One-pixel attack; Machine learning legislation (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:perchp:978-981-15-9255-3_7 Ordering information: This item can be ordered from DOI: 10.1007/978-981-15-9255-3_7 Access Statistics for this chapter More chapters in Perspectives in Law, Business and Innovation from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.