Ku Nurul Fazira Ku Azir
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Ku Nurul Fazira Ku Azir
Official Name
Ku Azir, Ku Nurul Fazira
Alternative Name
Ku Azir, Ku Nurul Fazira
Fazira Ku Azir, Ku Nurul
Azir, Ku
Azir, Ku Nurul Fazira Ku
Azir, K. N.F.K.
Ku Azir, K. N.F.
Azir, K. N.F.Ku
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Scopus Author ID
56879016500
Researcher ID
AAY-3466-2021

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  • Publication
    Enhancing security with multi-level steganography: a dynamic least significant bit and wavelet-based approach
    (International Information and Engineering Technology Association (IIETA), 2024-06-22)
    Mohammed Sabri Abuali
    ;
    Mohd Rashidi Che Beson
    ;
    Rafikha Aliana A Raof
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Safa Saad Hussein
    ;
    Ahmed Q. Abd-Alhasan
    This paper introduces a novel approach to enhancing multi-level security using steganography, a method of concealing information within non-secret data. This paper introduces an innovative approach to multi-level security enhancement using steganography, the art of concealing information within non-obvious data. Our proposed method uniquely combines Dynamic Least Significant Bit (DLSB) steganography with Wavelet Obtained Weights (WOW) steganographic algorithms, forging a sophisticated and adaptable system for secret data embedding. In our enhanced approach, we start by embedding text into an image using an optimized version of DLSB steganography. This refined technique adapts intelligently to the image’s local contrast, thereby preserving its visual quality and ensuring the integrity of the embedded information. Subsequently, the payload image is merged with a cover image through the WOW algorithm. This step optimally selects pixels for data embedding, creating a steganographic image that is virtually indistinguishable from the original. The novelty of our work lies in the seamless integration of these two advanced steganographic techniques, which significantly elevates the security and invisibility aspects beyond the current state-of-the-art methods in digital steganography. For validation, we utilized a pretrained MobileNet model to differentiate between original and stego images. This model plays a crucial role in demonstrating the indetectability of our method, achieving an impressive accuracy of 85% in distinguishing stego images from their originals. Our rigorous testing across various metrics — including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Bit Error Rate (BER), and Mean Squared Error (MSE) — showcases the effectiveness of our approach. The results indicate a robust performance, marking a significant advancement in secure digital communication. In this paper, we focus primarily on the detailed presentation of our results and the significant contributions of our current research, setting a strong foundation for future exploration in increasing robustness against steganalysis and improving the statistical invisibility of the steganography process.