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Found 5 papers, 0 papers with code
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction
The number of patches in a patch attack is variable and determines the attack's potency in a specific environment.
NSA: Naturalistic Support Artifact to Boost Network Confidence
Visual AI systems are vulnerable to natural and synthetic physical corruption in the real-world.
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models.
Soft Adversarial Training Can Retain Natural Accuracy
The focus of our work is to use abstract certification to extract a subset of inputs for (hence we call it 'soft') adversarial training.
Embedded Model Predictive Control Using Robust Penalty Method
To overcome the bottleneck of traditional quadratic programming (QP) solvers, this paper proposes a robust penalty method (RPM) to solve an optimization problem in a linear MPC.
