Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147413
Title: Special session on attacking and protecting Artificial Intelligence
Authors: Bhasin, Shivam
Garg, Siddarth
Regazzoni, Francesco
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2021
Source: Bhasin, S., Garg, S. & Regazzoni, F. (2021). Special session on attacking and protecting Artificial Intelligence. CAAI Transactions On Intelligence Technology, 6(1), 1-2. https://dx.doi.org/10.1049/cit2.12023
Journal: CAAI Transactions on Intelligence Technology 
Abstract: Modern artificial intelligence systems largely rely on advanced algorithms, including machine learning techniques such as deep learning. The research community has invested significant efforts in understanding these algorithms, optimally tuning them, and improving their performance, but it has mostly neglected the security facet of the problem. Recent attacks and exploits demonstrated that machine learning-based algorithms are susceptible to attacks targeting computer systems, including backdoors, hardware trojans and fault attacks, but are also susceptible to a range of attacks specifically targeting them, such as adversarial input perturbations. Implementations of machine learning algorithms are often crucial proprietary assets for companies thus need to be protected. It follows that implementations of artificial intelligence-based algorithms are an attractive target for piracy and illegitimate use and, as such, they need to be protected as all other IPs. This is equally important for machine learning algorithms running on remote servers vulnerable to micro-architectural exploits.
URI: https://hdl.handle.net/10356/147413
ISSN: 2468-2322
DOI: 10.1049/cit2.12023
Rights: © 2021 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:TL Journal Articles

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