Generative Information Hiding of Iris Feature Data via Gaussian Fuzzy Processing and Advanced Encryption Standard-Based Encryption

Conventional methods for hiding iris feature data have been hindered by inefficient feature extraction processes and inadequate post-hiding information security, thereby limiting both performance and applicability.To address these challenges, a generative information hiding approach for iris feature data has been developed, based on a Gaussian fuzzy algorithm and Advanced Encryption Standard (AES) encryption.In the preprocessing phase, a weighted averaging technique was employed for greyscale conversion, followed by Gaussian fuzzy smoothing to reduce noise while preserving structural integrity.Subsequent image sharpening was conducted using a Laplacian convolution kernel to enhance edge definition.The iris region was then localized and normalized to a fixed size, ensuring geometric invariance during feature extraction.Two-dimensional Gabor wavelets were utilized for the extraction of robust and discriminative iris features, given their proven effectiveness in capturing both spatial frequency and orientation information.To ensure data confidentiality, the extracted iris features were encrypted using the AES, with a corresponding decryption process integrated within the generative information hiding framework.This dual-layer strategy ensured that both the biometric feature data and the hidden information remained secure against unauthorized access or reconstruction.Experimental validation demonstrated that the proposed method enabled iris feature extraction within ten seconds on standard computing hardware, with a significantly improved data security coefficient compared to conventional techniques.Furthermore, the proposed methodology achieved a high level of imperceptibility and robustness, supporting its application in biometric security systems and privacy-preserving identity verification.These findings suggest that the integration of Gaussian fuzzy processing with secure encryption offers an effective pathway for reliable and efficient iris feature data concealment.

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Publication Details

Journal
Traitement du signal
Published
2025-08-13
DOI
https://doi.org/10.18280/ts.420422
Primary Topic
Biometric Identification and Security
Type
article
Field-Weighted Citation Impact
0.00

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article

Generative Information Hiding of Iris Feature Data via Gaussian Fuzzy Processing and Advanced Encryption Standard-Based Encryption

Shuchen Zhou, Dingyi Liu
Traitement du signal
Biometric Identification and Security
article

Generative Information Hiding of Iris Feature Data via Gaussian Fuzzy Processing and Advanced Encryption Standard-Based Encryption

Shuchen Zhou, Dingyi Liu
article en

Abstract

Conventional methods for hiding iris feature data have been hindered by inefficient feature extraction processes and inadequate post-hiding information security, thereby limiting both performance and applicability.To address these challenges, a generative information hiding approach for iris feature data has been developed, based on a Gaussian fuzzy algorithm and Advanced Encryption Standard (AES) encryption.In the preprocessing phase, a weighted averaging technique was employed for greyscale conversion, followed by Gaussian fuzzy smoothing to reduce noise while preserving structural integrity.Subsequent image sharpening was conducted using a Laplacian convolution kernel to enhance edge definition.The iris region was then localized and normalized to a fixed size, ensuring geometric invariance during feature extraction.Two-dimensional Gabor wavelets were utilized for the extraction of robust and discriminative iris features, given their proven effectiveness in capturing both spatial frequency and orientation information.To ensure data confidentiality, the extracted iris features were encrypted using the AES, with a corresponding decryption process integrated within the generative information hiding framework.This dual-layer strategy ensured that both the biometric feature data and the hidden information remained secure against unauthorized access or reconstruction.Experimental validation demonstrated that the proposed method enabled iris feature extraction within ten seconds on standard computing hardware, with a significantly improved data security coefficient compared to conventional techniques.Furthermore, the proposed methodology achieved a high level of imperceptibility and robustness, supporting its application in biometric security systems and privacy-preserving identity verification.These findings suggest that the integration of Gaussian fuzzy processing with secure encryption offers an effective pathway for reliable and efficient iris feature data concealment.

Traitement du signalVol. 42(4)
Huanghuai University
Openalex Percentile: Top 75%
Biometric Identification and Security
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