A Video-based Attack for Android Pattern Lock
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4028
- Type
- D - Journal article
- DOI
-
10.1145/3230740
- Title of journal
- ACM Transactions on Privacy and Security
- Article number
- 19
- First page
- -
- Volume
- 21
- Issue
- 4
- ISSN
- 2471-2566
- Open access status
- Technical exception
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
E - DSS (Distributed Systems and Services)
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper extends a highly cited NDSS-17 conference paper (16% acceptance rate). Pattern Lock is a security measure that protects mobile devices, being used by over 40% of Android device owners worldwide. It was the first work to show how the widely-used Android pattern lock can be attacked using computer vision algorithms. It thus identified a new vulnerability of pattern lock and graphical authentications of its kind. The work was featured on 200+ media outlets worldwide, including The Times, Daily Mail, and Forbes. UK National Cyber Security Centre (george.dickinson@nca.x.gsi.gov.uk) has since approached for using the techniques for law enforcement.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
