MarkIt: privacy markers for protecting visual secrets

84 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

I-Pic: A Platform for Privacy-Compliant Image Capture (2016, neutral): “These results demonstrate the necessity of diversity in privacy policy, and argue against venue based policies that cannot be customized for individuals [2, 5].”
A Design Space for Effective Privacy Notices (2015, background): “Markers can be placed on physical objects to control object or face recognition [109].”
Protecting Visual Information in Augmented Reality from Malicious Application Developers (2019, background): “Related works address these privacy concerns by taint-tracking and controlling information flow [8– 10, 14, 22] or adding an intermediate layer to preprocess frames and filter out sensitive visual information [4, 12, 20, 21].”
What You Mark is What Apps See (2016, methodology): “Due to these limitations, we have been investigating an alternative approach based on privacy markers [14].”
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, unknown): “Apart from detecting attributes, some works introduce new privacy challenges in vision such as adversarial perturbations [31, 35], privacy-preserving video capture [1, 36, 33, 37], person re-identification [2, 30], avoiding face detection [46, 16], full body re-identification [34] and privacy-sensit”