Stealing Deep Reinforcement Learning Models for Fun and Profit
Authors: Kangjie Chen, Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Yang Liu | Published: 2020-06-09 | Updated: 2020-12-22
Attack Type
Tricking Adversarial Attacks To Fail
Authors: Blerta Lindqvist | Published: 2020-06-08
On Universalized Adversarial and Invariant Perturbations
Authors: Sandesh Kamath, Amit Deshpande, K V Subrahmanyam | Published: 2020-06-08
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Authors: Hai Shu, Ronghua Shi, Qiran Jia, Hongtu Zhu, Ziqi Chen | Published: 2020-06-05 | Updated: 2022-05-08
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty
Authors: Tejas Jayashankar, Jonathan Le Roux, Pierre Moulin | Published: 2020-06-02 | Updated: 2020-09-15
Perturbation Analysis of Gradient-based Adversarial Attacks
Authors: Utku Ozbulak, Manvel Gasparyan, Wesley De Neve, Arnout Van Messem | Published: 2020-06-02
Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense
Authors: Jay N. Paranjape, Rahul Kumar Dubey, Vijendran V Gopalan | Published: 2020-06-02
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
Authors: Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, Yanzhao Wu | Published: 2020-04-22 | Updated: 2020-04-23
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers
Authors: Yusi Lei, Sen Chen, Lingling Fan, Fu Song, Yang Liu | Published: 2020-04-15
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Authors: Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla | Published: 2020-03-28 | Updated: 2020-08-19