7 citations · 12 across the 4 of their papers we have counts for
8 papers
Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks
Kenneth T. Co, David Martinez-Rego, Zhongyuan Hau +1
Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integra…
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…
Robustness and Transferability of Universal Attacks on Compressed Models
Alberto G. Matachana, Kenneth T. Co, Luis Muñoz-González +2
Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge devices. However, DNNs remain vulne…
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
Luis Muñoz-González, Kenneth T. Co, Emil C. Lupu
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learni…