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Found 4 papers, 2 papers with code
Leveraging Disentangled Representations to Improve Vision-Based Keystroke Inference Attacks Under Low Data
We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover.
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks
We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover.
Revisiting the Threat Space for Vision-based Keystroke Inference Attacks
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.
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks.
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