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Found 3 papers, 2 papers with code
Physical world assistive signals for deep neural network classifiers -- neither defense nor attack
We discuss how we can exploit these insights to re-think, or avoid, some patterns that might contribute to, or degrade, the detectability of objects in the real-world.
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty
We propose three metrics to determine the proportion of robust images in a dataset and provide scoring to determine the dataset bias.
Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space
Our results show that our approach achieves the best balance between defence against adversarial attacks such as FGSM, PGD and DDN and maintaining the original accuracies of VGG-16, ResNet50 and DenseNet121 on clean images.
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