Selective Jpeg2000 Encryption of Iris Data: Protecting Sample Data vs. Normalised Texture

Rieger, Martin; Hämmerle-Uhl, Jutta; Uhl, Andreas

doi:10.1109/icassp.2019.8683196

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…To detect the encryption location in the codestream with the biggest impact on the biometric recognition we are applying a "Sliding Window Encryption" [8,9,10,11]. In this method a window, i.e., a certain continuous percentage of the codestream size, is encrypted starting at a given offset.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Highly Efficient Protection of Biometric Face Samples with Selective JPEG2000 Encryption

Hofbauer

Martínez-Díaz

Kirchgasser

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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When biometric databases grow larger, a security breach or leak can affect millions. In order to protect against such a threat, the use of encryption is a natural choice. However, a biometric identification attempt then requires the decryption of a potential huge database, making a traditional approach potentially unfeasible. The use of selective JPEG2000 encryption can reduce the encryption's computational load and enable a secure storage of biometric sample data. In this paper we will show that selective encryption of face biometric samples is secure. We analyze various encoding settings of JPEG2000, selective encryption parameters on the "Labeled Faces in the Wild" database and apply several traditional and deep learning based face recognition methods.

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Section: Evaluation Methodologymentioning

confidence: 99%

Highly Efficient Protection of Biometric Face Samples with Selective JPEG2000 Encryption

Hofbauer

Martínez-Díaz

Kirchgasser

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Only first attempts have been made into this direction: [5] applies SIFT keypoint detection to encrypted data and computes distance to SIFT keypoints extracted from original data while [6] considers segmentation performance on encrypted data as a metric determine encryption strength. Biometric recognition has also been applied to encrypted sample data in the context of fingerprint [7], finger vein [8], and iris [9] recognition, respectively. However, these recognition results have not been related to any IQM applied to the encrypted biometric sample data.…”

Section: Introductionmentioning

confidence: 99%

Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples

Rieger

Hämmerle-Uhl

Uhl

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.

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Evaluation of Encryption and Decryption Algorithms Efficiency for Safeguarding Pictures

Upender,

Kumar,

Saritha

et al. 2024

Lecture Notes in Networks and Systems

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Selective Jpeg2000 Encryption of Iris Data: Protecting Sample Data vs. Normalised Texture

Cited by 5 publications

References 14 publications

Highly Efficient Protection of Biometric Face Samples with Selective JPEG2000 Encryption

Highly Efficient Protection of Biometric Face Samples with Selective JPEG2000 Encryption

Security Assessment of Selectively Encrypted Visual Data: Iris Recognition on Protected Samples

Evaluation of Encryption and Decryption Algorithms Efficiency for Safeguarding Pictures

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