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. 2019 Jul 6;19(13):2985.
doi: 10.3390/s19132985.

A Cancelable Iris- and Steganography-Based User Authentication System for the Internet of Things

Wencheng Yang  1 , Song Wang  2 , Jiankun Hu  3 , Ahmed Ibrahim  4 , Guanglou Zheng  4 , Marcelo Jose Macedo  4 , Michael N Johnstone  4 , Craig Valli  4
Affiliations

A Cancelable Iris- and Steganography-Based User Authentication System for the Internet of Things

Wencheng Yang et al. Sensors (Basel). .

Abstract

Remote user authentication for Internet of Things (IoT) devices is critical to IoT security, as it helps prevent unauthorized access to IoT networks. Biometrics is an appealing authentication technique due to its advantages over traditional password-based authentication. However, the protection of biometric data itself is also important, as original biometric data cannot be replaced or reissued if compromised. In this paper, we propose a cancelable iris- and steganography-based user authentication system to provide user authentication and secure the original iris data. Most of the existing cancelable iris biometric systems need a user-specific key to guide feature transformation, e.g., permutation or random projection, which is also known as key-dependent transformation. One issue associated with key-dependent transformations is that if the user-specific key is compromised, some useful information can be leaked and exploited by adversaries to restore the original iris feature data. To mitigate this risk, the proposed scheme enhances system security by integrating an effective information-hiding technique-steganography. By concealing the user-specific key, the threat of key exposure-related attacks, e.g., attacks via record multiplicity, can be defused, thus heightening the overall system security and complementing the protection offered by cancelable biometric techniques.

Keywords: cancelable; feature data protection; iris; steganography.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A partial taxonomy of authentication schemes for IoT devices (adapted from [6]).
Figure 2
Figure 2
The entire authentication process of the proposed system.
Figure 3
Figure 3
Three iris feature generation steps—Step 1: segmentation, Step 2: normalization, and Step 3: feature extraction.
Figure 4
Figure 4
An image before key hiding (left) and after key hiding (right). The difference between them is impossible to distinguish with the naked eye.
Figure 5
Figure 5
Three iris samples from the CASIA-IrisV3-Interval, MMU-V1, and UBIRIS-V1-Session 1 databases, in (a), (b), and (c), respectively.
Figure 6
Figure 6
System performance under different score thresholds, with m = 500 on the CASIA-IrisV3-Interval database. FRR, false rejection rate; FAR, false acceptance rate.
Figure 7
Figure 7
System performance under different score thresholds, with m = 1000 on the CASIA-IrisV3-Interval database.
Figure 8
Figure 8
System performance under different score thresholds, with m = 2000 on the CASIA-IrisV3-Interval database.
Figure 9
Figure 9
Distribution of the similarity scores of imposter tests with different feature dimensions on the CASIA-IrisV3-Interval database.
See this image and copyright information in PMC

References

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