Using Non-invertible Data Transformations to Build Adversary-Resistant Deep Neural Networks

By Qinglong Wang, Wenbo Guo, Alexander Ororbia, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong. 2016

10 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Naveed Akhtar
2. Nicolas Papernot
3. Ajmal Saeed Mian
4. Patrick Drew McDaniel
5. Kathrin Grosse
6. Nicholas Carlini
7. Michael Backes
8. Florian Tramèr

Citations in the corpus

• Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018, background): “[129], [122] developed adversary resistant neural networks by leveraging non-invertible data transformation in the network.”
• Adversarial Examples for Malware Detection (2017, background): “Other approaches include blocking the gradient flow [37], changing the activation function [17], or directly classifying adversarial examples as out of distribution [7,22,13,10].”