Adversarial Attacks for Salient Object Detection
Published: 5 August 2024| Version 1 | DOI: 10.17632/98zpfhg7vn.1
Contributors:
Matthieu Olague, , , Description
This dataset is an image repository that contains five different image databases to evaluate adversarial robustness in Salient Object Detection (SOD) through the introduction of 12 adversarial examples, each leveraging a known adversarial attack or noise perturbation. The dataset comprises 56,387 digital images, resulting from applying adversarial examples on subsets of four standard databases (i.e. FT, PASCAL-S, ImgSal, DUTS), and a fifth database (SNPL) portraying a real-world visual attention problem of a shorebird called the Snowy Plover. Original and rescaled images from the five databases used with the adversarial examples are also included as part of this dataset for ease of access and explainability.
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Categories
Computer Vision, Pattern Recognition, Convolutional Neural Network, Symbolic Learning, Adversarial Example, Salient Object Detection