Abstract

In the digital era we live, trustworthy verification schemes are required to ensure security and to authenticate the identity of an individual. Traditional passwords were proved to be highly vulnerable to attacks and the need of adopting new verification schemes is compulsory. Biometric factors have gained a lot of interest during the last years due to their uniqueness, ease of use, user convenience, and ease of deployment. However, recent research showed that even this unique authentication factors are not inviolable techniques. Thus, it is necessary to employ new verification schemes that cannot be replicated or stolen. In this paper we propose the utilization of steganography as a tool to provide unbreakable passwords. More specifically, we obtain a biometric feature of a user and embed it as a hidden message in an image. This image is then utilized as a password, the so-called StegoPass. Reversely, when a legit user or an attacker tries to unlock a device or an application, the same biometric feature is captured and embedded with the same steganography algorithm into the picture. The hash key of the resulted stego image in both cases is produced and if there is a complete match, user is considered as authenticated. To ensure that the proposed StegoPass cannot be replicated, we have conducted experiments with state-of-the-art deep learning algorithms. Moreover, it was examined whether Generative Adversarial Networks could produce exact copies of the StegoPass to fool the suggested method and the results showed that the proposed verification scheme is extremely secure.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.