160 citations · 248 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…