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Found 4 papers, 3 papers with code
Adversarial Attack Against Image-Based Localization Neural Networks
In this paper, we present a proof of concept for adversarially attacking the image-based localization module of an autonomous vehicle.
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training
In this paper, we introduce a novel neural network training framework that increases model's adversarial robustness to adversarial attacks while maintaining high clean accuracy by combining contrastive learning (CL) with adversarial training (AT).
Stealing Black-Box Functionality Using The Deep Neural Tree Architecture
This paper makes a substantial step towards cloning the functionality of black-box models by introducing a Machine learning (ML) architecture named Deep Neural Trees (DNTs).
Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks
In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that in some cases may be unnoticeable by human observers and is implementable in the real world.
