The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks

17 June 2019

Papers citing "The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks"

5 / 5 papers shown

Title
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing Andreas Bär Jonas Löhdefink Nikhil Kapoor Serin Varghese Fabian Hüger Peter Schlicht Tim Fingscheidt AAML 113 33 0 11 Jan 2021
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation Marvin Klingner Andreas Bär Tim Fingscheidt AAML 35 40 0 23 Apr 2020
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study David Mickisch F. Assion Florens Greßner W. Günther M. Motta AAML 19 34 0 05 Feb 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 303 3,115 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 335 5,849 0 08 Jul 2016