FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning

4 November 2018

Papers citing "FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning"

7 / 7 papers shown

Title
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems Shail Dave Alberto Marchisio Muhammad Abdullah Hanif Amira Guesmi Aviral Shrivastava Ihsen Alouani Mohamed Bennai 34 13 0 18 Apr 2022
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks Alberto Marchisio Giacomo Pira Maurizio Martina Guido Masera Mohamed Bennai AAML 36 30 0 01 Jul 2021
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead Mohamed Bennai Mahum Naseer T. Theocharides C. Kyrkou O. Mutlu Lois Orosa Jungwook Choi OOD 81 100 0 04 Jan 2021
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead Maurizio Capra Beatrice Bussolino Alberto Marchisio Guido Masera Maurizio Martina Mohamed Bennai BDL 59 140 0 21 Dec 2020
RED-Attack: Resource Efficient Decision based Attack for Machine Learning Faiq Khalid Hassan Ali Muhammad Abdullah Hanif Semeen Rehman Rehan Ahmed Mohamed Bennai AAML 31 14 0 29 Jan 2019
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks Faiq Khalid Hassan Ali Hammad Tariq Muhammad Abdullah Hanif Semeen Rehman Rehan Ahmed Mohamed Bennai AAML MQ 35 37 0 04 Nov 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 317 5,847 0 08 Jul 2016