all AI news
Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
April 19, 2024, 4:41 a.m. | Mohammad Javad Askarizadeh, Ebrahim Farahmand, Jorge Castro-Godinez, Ali Mahani, Laura Cabrera-Quiros, Carlos Salazar-Garcia
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving. However, their high computational complexity and vulnerability to adversarial attacks are ongoing challenges. In this letter, approximate multipliers are used to explore DNN robustness improvement against adversarial attacks. By uniformly replacing accurate multipliers for state-of-the-art approximate ones in DNN layer models, we explore the DNNs robustness against various adversarial attacks in a feasible time. Results show up to 7% …
abstract advanced adversarial adversarial attacks applications arxiv attacks autonomous autonomous driving challenges complexity computational cs.cr cs.lg dnn driving explore healthcare however improvement networks neural networks robustness type vulnerability world
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Business Data Analyst
@ Alstom | Johannesburg, GT, ZA