Osadchy, Margarita, Hernandez-Castro, Julio C., Gibson, Stuart J., Dunkelman, Orr, Perez-Cabo, Daniel (2017) No Bot Expects the DeepCAPTCHA! Introducing Immutable Adversarial Examples, with Applications to CAPTCHA Generation. IEEE Transactions on Information Forensics and Security, 12 (11). pp. 2640-2653. ISSN 1556-6013. E-ISSN 1556-6021. (doi:10.1109/TIFS.2017.2718479) (KAR id:62081)
PDF
Author's Accepted Manuscript
Language: English |
|
Download this file (PDF/2MB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: http://dx.doi.org/10.1109/TIFS.2017.2718479 |
Abstract
Recent advances in Deep Learning (DL) allow for solving complex AI problems that used to be considered very hard. While this progress has advanced many fields, it is considered to be bad news for CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart), the security of which rests on the hardness of some learning problems.
In this paper we introduce DeepCAPTCHA, a new and secure CAPTCHA scheme based on adversarial examples, an inherit limitation of the current Deep Learning networks. These adversarial examples are constructed inputs, either synthesized from scratch or computed by adding a small and specific perturbation called adversarial noise to correctly classified items, causing the targeted DL network to misclassify them. We show that plain adversarial noise is insufficient to achieve secure CAPTCHA schemes, which leads us to introduce immutable adversarial noise — an adversarial noise that is resistant to removal attempts. In this work we implement a proof of concept system, and its analysis shows that the scheme offers high security and good usability compared to the best previously existing CAPTCHAs.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1109/TIFS.2017.2718479 |
Uncontrolled keywords: | CAPTCHA, Deep Learning, CNN, Adversarial examples, HIP |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing Divisions > Division of Natural Sciences > Physics and Astronomy |
Depositing User: | Stuart Gibson |
Date Deposited: | 15 Jun 2017 13:08 UTC |
Last Modified: | 09 Dec 2022 05:26 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/62081 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):