160 citations · 248 across the 8 of their papers we have counts for
9 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…
Universal Adversarial Robustness of Texture and Shape-Biased Models
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +2
Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture a…
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…
Sensitivity of Deep Convolutional Networks to Gabor Noise
Kenneth T. Co, Luis Muñoz-González, Emil C. Lupu
Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions o…
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Kenneth T. Co, Luis Muñoz-González, Sixte de Maupeou +1
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning a…