Spacetime SSM forecasters represent optimal Kalman predictors for autoregressive data but remain vulnerable to model-free attacks that exploit local linearity and increase error by over 33% compared to projected gradient descent.
Towards deep learning models resistant to adversarial attacks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Modern imitation learning methods including Diffusion Policy and Implicit Behavior Cloning are highly vulnerable to universal adversarial perturbations, with successful black-box transfer attacks across algorithms.
MalPurifier combines diversified adversarial perturbations, protective noise injection, and a denoising autoencoder with dual loss to defend Android malware detectors, reporting over 90.91% robust accuracy against 37 evasion attacks on two datasets.
citing papers explorer
-
Adversarial Robustness of Deep State Space Models for Forecasting
Spacetime SSM forecasters represent optimal Kalman predictors for autoregressive data but remain vulnerable to model-free attacks that exploit local linearity and increase error by over 33% compared to projected gradient descent.
-
How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
Modern imitation learning methods including Diffusion Policy and Implicit Behavior Cloning are highly vulnerable to universal adversarial perturbations, with successful black-box transfer attacks across algorithms.
-
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
MalPurifier combines diversified adversarial perturbations, protective noise injection, and a denoising autoencoder with dual loss to defend Android malware detectors, reporting over 90.91% robust accuracy against 37 evasion attacks on two datasets.