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Found 6 papers, 0 papers with code
Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label Noise
Motivated by the fact that overfitted neural networks tend to rather memorize noise in the training data than generalize to unseen data, we examine how the training accuracy changes in the presence of increasing data perturbations and study the connection to overfitting.
Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection
Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concentrated in a particular region of an input image.
Adversarial Vulnerability of Temporal Feature Networks for Object Detection
In this work, we study whether temporal feature networks for object detection are vulnerable to universal adversarial attacks.
Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detection
We have evaluated two approaches to generate naturalistic patches: by incorporating patch generation into the GAN training process and by using the pretrained GAN.
Is Neuron Coverage Needed to Make Person Detection More Robust?
Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
