Anti-Shoulder Surfing Login Based on Multi-Entry Models on Onscreen Keyboard

Shoulder surfing is a common threat used to steal sensitive information, specifically credential and PIN number either by human or recorder cameras. The proposed solution is designed for both PCs and smartphone-based login interfaces. In this paper, we propose a new authentication scheme based on augmented misinformation existing techniques in addition to the introduction of a novel one for maximizing security level and keeping it simple to use. We reuse the crossing-based technique with keys layout randomisation support in addition to displacement factor consideration. Moreover, we introduce white keys-based technique that is used for increasing the security level when keeping the same difficulty-level of exploitation. The evaluation results show that the proposed scheme outperforms well known methods of the state of the art and gives a trade-off between the usability and the security levels against active shoulder surfing attacks using camera recorder.