160 citations · 598 across the 22 of their papers we have counts for
4 papers · 1 filter
Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine learning algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to degrade the algorithms' performance. We show that current a…
Real-time Detection of Practical Universal Adversarial Perturbations
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +1
Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attac…
Jacobian Regularization for Mitigating Universal Adversarial Perturbations
Kenneth T. Co, David Martinez Rego, Emil C. Lupu
Universal Adversarial Perturbations (UAPs) are input perturbations that can fool a neural network on large sets of data. They are a class of attacks that represents a significant t…
Object Removal Attacks on LiDAR-based 3D Object Detectors
Zhongyuan Hau, Kenneth T. Co, Soteris Demetriou +1
LiDARs play a critical role in Autonomous Vehicles' (AVs) perception and their safe operations. Recent works have demonstrated that it is possible to spoof LiDAR return signals to…