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Found 4 papers, 2 papers with code
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems
While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model's output on a set of inputs.
Colored Noise Injection for Training Adversarially Robust Neural Networks
Even though deep learning has shown unmatched performance on various tasks, neural networks have been shown to be vulnerable to small adversarial perturbations of the input that lead to significant performance degradation.
On the generalization of bayesian deep nets for multi-class classification
Generalization bounds which assess the difference between the true risk and the empirical risk have been studied extensively.
Smoothed Inference for Adversarially-Trained Models
In this work, we study the application of randomized smoothing as a way to improve performance on unperturbed data as well as to increase robustness to adversarial attacks.
