271 citations · 841 across the 29 of their papers we have counts for
5 papers · 1 filter
A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations
Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer +1
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperce…
Adversarially Robust Classification based on GLRT
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore…
Multi-sensor Spatial Association using Joint Range-Doppler Features
Anant Gupta, Ahmet Dundar Sezer, Upamanyu Madhow
We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such "single snapshot imaging" provides timely situati…
Polarizing Front Ends for Robust CNNs
Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic +2
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up str…
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors
Metehan Cekic, Soorya Gopalakrishnan, Upamanyu Madhow
Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variatio…