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Found 6 papers, 4 papers with code
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Adaptive defenses, which optimize at test time, promise to improve adversarial robustness.
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
Many optimization methods for generating black-box adversarial examples have been proposed, but the aspect of initializing said optimizers has not been considered in much detail.
Leveraging Semantic Embeddings for Safety-Critical Applications
Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning.
Towards Safety Verification of Direct Perception Neural Networks
We study the problem of safety verification of direct perception neural networks, where camera images are used as inputs to produce high-level features for autonomous vehicles to make control decisions.
Graph Neural Networks for Modelling Traffic Participant Interaction
We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account.
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
We consider adversarial examples for image classification in the black-box decision-based setting.
