Yanjun Qi

TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.

Abstract: Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purposeful distortions to natural examples. Previous studies to defend against adversarial examples mostly focused on refining the DNN models, but have either shown limited success or required expensive computation. We propose a new strategy, \emph{feature squeezing}, that can be used to harden DNN models by detecting adversarial examples. Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. By comparing a DNN model's prediction on the original input with that on squeezed inputs, feature squeezing detects adversarial examples with high accuracy and few false positives. This paper explores two feature squeezing methods: reducing the color bit depth of each pixel and spatial smoothing. These simple strategies are inexpensive and complementary to other defenses, and can be combined in a joint detection framework to achieve high detection rates against state-of-the-art attacks.

TL;DR: Two feature squeezing methods are explored: reducing the color bit depth of each pixel and spatial smoothing, which are inexpensive and complementary to other defenses, and can be combined in a joint detection framework to achieve high detection rates against state-of-the-art attacks.

TL;DR: DeepWordBug as mentioned in this paper generates small text perturbations in a black-box setting that force a deep-learning classifier to misclassify a text input by scoring strategies to find the most important words to modify.

TL;DR: The Random Forest technique, which includes an ensemble of decision trees and incorporates feature selection and interactions naturally in the learning process, is a popular choice because it is nonparametric, interpretable, efficient, and has high prediction accuracy for many types of data.