Protecting against screenshots: An image processing approach

Author: Alex Yong-Sang Chia, Udana Bandara, Xiangyu Wang, Hiromi Hirano


Motivated by reasons related to data security and privacy, we propose a method to limit meaningful visual contents of a display from being captured by screenshots. Traditional methods take a system architectural approach to protect against screenshots. We depart from this framework, and instead exploit image processing techniques to distort visual data of a display and present the distorted data to the viewer. Given that a screenshot captures distorted visual contents, it yields limited useful data. We exploit the human visual system to empower viewers to automatically and mentally recover the distorted contents into a meaningful form in real-time. Towards this end, we leverage on findings from psychological studies which show that blending of visual information from recent and current fixations enables human to form meaningful representation of a scene. We model this blending of information by an additive process, and exploit this to design a visual contents distortion algorithm that supports real-time contents recovery by the human visual system. Our experiments and user study demonstrate the feasibility of our method to allow viewers to readily interpret visual contents of a display, while limiting meaningful contents from being captured by screenshots.

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