134 citations · 258 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
Hossein Hosseini, Yize Chen, Sreeram Kannan +2
Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars. To further broa…
Deceiving Google's Perspective API Built for Detecting Toxic Comments
Hossein Hosseini, Sreeram Kannan, Baosen Zhang +1
Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently,…
Learning Temporal Dependence from Time-Series Data with Latent Variables
Hossein Hosseini, Sreeram Kannan, Baosen Zhang +1
We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relatio…