Revisiting the Threat Space for Vision-based Keystroke Inference Attacks
This work addresses a security vulnerability for mobile device users by enabling more effective attacks through synthetic data, though it is incremental as it builds on existing adversarial domain adaptation techniques.
The authors tackled the problem of outdated threat models for vision-based keystroke inference attacks by creating a large-scale synthetic dataset to train deep neural networks, achieving 95.6% accuracy in single keypress classification after pre-training on synthetic data and fine-tuning on real data.
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past, but we argue that the defining characteristics for this threat space, namely the strength of the attacker, are outdated.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. We show that first pre-training on synthetic data, followed by adopting transfer learning techniques on real-life data, increases the performance of our deep learning models. This indicates that these models are able to learn rich, meaningful representations from our synthetic data and that training on the synthetic data can help overcome the issue of having small, real-life datasets for vision-based key stroke inference attacks. For this work, we focus on single keypress classification where the input is a frame of a keypress and the output is a predicted key. We are able to get an accuracy of 95.6% after pre-training a CNN on our synthetic data and training on a small set of real-life data in an adversarial domain adaptation framework. Source Code for Simulator: https://github.com/jlim13/keystroke-inference-attack-synthetic-dataset-generator-