Adversarial Attacks for Low Light Image Enhancement
Published: 15 January 2025| Version 1 | DOI: 10.17632/n2j4yfnt3x.1
Contributors:
, , , , Description
This dataset is an image repository that evaluates adversarial robustness by introducing 12 adversarial examples, each leveraging a known adversarial attack or noise perturbation. The repository contains a total of 4970 digital images through the application of AEs to five different image databases (i.e., MEF, LIME, LOLv1, LOLv2-R, LOLv2-S) that include pictures of landscapes, objects, or locations with low, normal, or high illumination conditions. We incorporate original images from the five databases utilized alongside the adversarial examples to facilitate easy access and distribution of this dataset.
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Institutions
Centro de Investigacion Cientifica y de Educacion Superior de Ensenada
Categories
Computer Vision, Image Enhancement, Light Intensity, Deep Learning, Adversarial Example
Funding
Ensenada Center for Scientific Research and Higher Education
634-135