Revisiting the Threat Space for Vision-based Keystroke Inference Attacks

Understand

A vision-based keystroke inference attack is a side-channel attack in which an attacker uses an optical device to record users on their mobile devices and infer their keystrokes.

• The threat space for these attacks has been studied in the past, but we argue that the defining characteristics for this threat space, namely the strength of the attacker, are outdated.
• Previous works do not study adversaries with vision systems that have been trained with deep neural networks because these models require large amounts of training data and curating such a dataset is expensive.
• To address this, we create a large-scale synthetic dataset to simulate the attack scenario for a keystroke inference attack.