134 citations · 234 across the 4 of their papers we have counts for
8 papers
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…
Assessing Shape Bias Property of Convolutional Neural Networks
Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal +1
It is known that humans display "shape bias" when classifying new items, i.e., they prefer to categorize objects based on their shape rather than color. Convolutional Neural Networ…
Semantic Adversarial Examples
Hossein Hosseini, Radha Poovendran
Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mos…
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API
Hossein Hosseini, Baicen Xiao, Andrew Clark +1
Due to the growth of video data on Internet, automatic video analysis has gained a lot of attention from academia as well as companies such as Facebook, Twitter and Google. In this…
Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos
Hossein Hosseini, Baicen Xiao, Radha Poovendran
Despite the rapid progress of the techniques for image classification, video annotation has remained a challenging task. Automated video annotation would be a breakthrough technolo…