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Found 8 papers, 1 papers with code
Generating Universal Adversarial Perturbations for Quantum Classifiers
In this work, we introduce QuGAP: a novel framework for generating UAPs for quantum classifiers.
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.
Do we need entire training data for adversarial training?
We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training.
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.
Spiking Approximations of the MaxPooling Operation in Deep SNNs
MaxPooling layers in Convolutional Neural Networks (CNNs) are an integral component to downsample the intermediate feature maps and introduce translational invariance, but the absence of their hardware-friendly spiking equivalents limits such CNNs' conversion to deep SNNs.
Deep Learning for System Trace Restoration
The output of the model will be a close reconstruction of the true data, and can be fed to algorithms that rely on clean data.
