Augmented Reality-based Mimicry Attacks on Behaviour-Based Smartphone Authentication
Why this work is in the frame
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Bibliographic record
Abstract
We develop an augmented reality-based app that resides on the attacker's smartphone and leverages computer vision and raw input data to provide real-time mimicry attack guidance on the victim's phone. Our approach does not require tampering or installing software on the victim's device, or specialized hardware. The app is demonstrated by attacking keystroke dynamics, a method leveraging the unique typing behaviour of users to authenticate them on a smartphone, which was previously thought to be hard to mimic. In addition, we propose a low-tech AR-like audiovisual method based on spatial pointers on a transparent film and audio cues. We conduct experiments with 31 participants and mount over 400 attacks to show that our methods enable attackers to successfully bypass keystroke dynamics for 87% of the attacks after an average mimicry training of four minutes. Our AR-based method can be extended to attack other input behaviour-based biometrics. While the particular attack we describe is relatively narrow, it is a good example of using AR guidance to enable successful mimicry of user behaviour---an approach of increasing concern as AR functionality becomes more commonplace.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it