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Found 6 papers, 5 papers with code
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they remain adversarial even against other models.
Explainable Machine Learning in Deployment
Yet there is little understanding of how organizations use these methods in practice.
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.
