Mohammad N. AlShehri

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In this work, we evaluate the use computational models of visual attention to predict graphical passwords. We compare the performance of three models: the Itti-Koch-Niebur model, Graph-Based Visual Saliency (GBVS), and the Image Signature. The visual attention maps generated by the models are compared to ground truth user password location selections.(More)
This work proposes a metric for determining the suitability of guiding images for graphical passwords. This measurement is intended to favor images that encourage users to select memorable, but non-obvious password click points. The metric was developed by analyzing thousands of passwords on a small but varied image database. We found that saliency covered(More)
A graphical password guiding image serves as a visual prompt to improve password memorability. However, passwords may be easily guessed if the guiding image contains hotspots, or commonly chosen (e.g., 'clickable') points that are predictable via automated means. In this paper, we propose a method to determine graphical password guiding image suitability in(More)
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