134 citations · 419 across the 67 of their papers we have counts for
3 papers · 2 filters
Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples
Hossein Hosseini, Sreeram Kannan, Radha Poovendran
Deep learning classifiers are known to be vulnerable to adversarial examples. A recent paper presented at ICML 2019 proposed a statistical test detection method based on the observ…
Potential-Based Advice for Stochastic Policy Learning
Baicen Xiao, Bhaskar Ramasubramanian, Andrew Clark +3
This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies. We show…
Dropping Pixels for Adversarial Robustness
Hossein Hosseini, Sreeram Kannan, Radha Poovendran
Deep neural networks are vulnerable against adversarial examples. In this paper, we propose to train and test the networks with randomly subsampled images with high drop rates. We…