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Found 9 papers, 5 papers with code
Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks
In this work, we propose a novel approach, Jacobian Ensembles-a combination of Jacobian regularization and model ensembles to significantly increase the robustness against UAPs whilst maintaining or improving model accuracy.
Real-time Detection of Practical Universal Adversarial Perturbations
Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attacks against Deep Neural Networks (DNNs).
Jacobian Regularization for Mitigating Universal Adversarial Perturbations
Universal Adversarial Perturbations (UAPs) are input perturbations that can fool a neural network on large sets of data.
Object Removal Attacks on LiDAR-based 3D Object Detectors
LiDARs play a critical role in Autonomous Vehicles' (AVs) perception and their safe operations.
Robustness and Transferability of Universal Attacks on Compressed Models
In this work, we analyze the effect of various compression techniques to UAP attacks, including different forms of pruning and quantization.
Universal Adversarial Robustness of Texture and Shape-Biased Models
Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise.
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets.
Sensitivity of Deep Convolutional Networks to Gabor Noise
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 of a dataset.
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time.
