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Found 8 papers, 6 papers with code
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
We believe that level 1 (AdEx on a unified dataset) should be added to any OOD detector to see the limitations.
Adversarial Examples are Misaligned in Diffusion Model Manifolds
In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results.
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images.
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks.
Visual Prompting for Adversarial Robustness
In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time.
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
In its most commonly reported sub-task, RobustBench evaluates and ranks the adversarial robustness of trained neural networks on CIFAR10 under AutoAttack (Croce and Hein 2020b) with l-inf perturbations limited to eps = 8/255.
Detecting AutoAttack Perturbations in the Frequency Domain
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention.
The RoboCup Rescue Victim Dataset
Lots of face detection approaches can be found in literature, which manly are used for human face recognition.
