Application of bi-orthogonal wavelet transform and genetic algorithm in image steganography

Pramanik, Sabyasachi; Singh, Ravinder Pal; Ghosh, Ram Krishna

doi:10.1007/s11042-020-08676-1

Cited by 48 publications

(6 citation statements)

References 18 publications

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“…[1] 2020 IWT, PSO PSNR=51.1468 SSIM=0.9982 QI=0.9999 [2] 2022 IWT, GA PSNR=62.04 SSIM=0.9977 [3] 2021 IWT, Fractal Encryption PSNR=53.869 SSIM=0.9846 [4] 2021 IWT, Cyclic Chaos system, Neural Network PSNR=58.432 SSIM=0.9954 [5] 2022 IWT, MSB PSNR=60.866 MSE=0.053 AD=0.0430 [6] 2023 IWT, GA PSNR=62.57 SSIM=0.9861 CC=0.9865 [7] 2020…”

Section: Ref Year Image Steganography Technique Metricsmentioning

confidence: 99%

A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

Thangaraju,

2024

Preprint

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Image steganography comprises concealing sensitive data inside digital images to ensure secure communication. In this work, a ground-breaking method for image steganography is introduced, which fuses chaotic and genetic algorithms, aiming to create a strong and impenetrable data concealment technique. The study applies Integer Wavelet Transform (IWT) to the image and services logistic map chaotic system to produce pseudo-random sequences. These sequences take a significant in identifying the most secure positions within the image to embed confidential message bits. This embedding process is highly secure and resilient against statistical attacks. Moreover, a genetic algorithm optimizes this process, bolstering the security of the steganography approach. Using a population-based search technique, the genetic algorithm efficiently explores the search space, identifying the optimal chromosome representation for data embedding. This significantly enhances the embedding capacity while minimizing distortion to the original image. Through extensive experimental evaluations and performance analyses, the study showcases the effectiveness and resilience of this proposed approach. The findings highlight that combining chaotic and genetic algorithms in image steganography markedly improves security, capacity, and resistance to attacks when compared to traditional methods. This steganographic scheme achieved a PSNR of 55.3392 dB, a MSE of 0.3620, a SSIM of 0.9744, and a VIF of 0.9269. Additionally, the work demonstrated a hidden information capacity of 3.67 bits per pixel (BPP).

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Section: Ref Year Image Steganography Technique Metricsmentioning

confidence: 99%

A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

Thangaraju,

2024

Preprint

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“…This approach employs a criterion centered on edge intensity, derived from localized neighborhood analysis, to pinpoint the optimal embedding coefficients. Likewise, in [7], the same author introduced a strategy that utilizes a combination of bi-orthogonal wavelet transform and GA. The bi-orthogonal wavelet transform dissects the cover image into its wavelet domain, yielding a multi-resolution representation and frequency sub-bands that accommodate data embedding.…”

Section: (Vii) Randomized Techniquesmentioning

confidence: 99%

A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

Thangaraju,

2024

Preprint

View full text Add to dashboard Cite

Image steganography comprises concealing sensitive data inside digital images to ensure secure communication. In this research we introduce a ground-breaking method for image steganography that fuses chaotic and genetic algorithms, aiming to create a strong and impenetrable data concealment technique. The study applies Integer Wavelet Transform (IWT) to the image and services logistic map chaotic system to produce pseudo-random sequences. These sequences take a significant in identifying the most secure positions within the image to embed confidential message bits. This embedding process is highly secure and resilient against statistical attacks. Moreover, a genetic algorithm optimizes this process, bolstering the security of the steganography approach. Using a population-based search technique, the genetic algorithm efficiently explores the search space, identifying the optimal chromosome representation for data embedding. This significantly enhances the embedding capacity while minimizing distortion to the original image. Through extensive experimental evaluations and performance analyses, the study showcases the effectiveness and resilience of this proposed approach. The findings highlight that combining chaotic and genetic algorithms in image steganography markedly improves security, capacity, and resistance to attacks when compared to traditional methods.

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“…For this purpose, the first two key-value pair of the JSON contains the number of payload and cover images. Followed by this, the child of the nested JSON objects contain details of each image [17][18]. In these, the metadata contains the shape of the original payload image including height, width, colormaps, number of chunks, etc.…”

Section: The Mapping Graph and Json Key-value Pair Dictionarymentioning

confidence: 99%

Images Within Images? A Multi-image Paradigm with Novel Key-Value Graph Oriented Steganography

Adhikary¹

2022

Intelligent Computing &Amp; Optimization

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Steganographic methods have been in the limelight of research and development for concealing secret data within a cover media without being noticed through general visualization. The Least Significant Bits (LSBs) of 8bit color code for the RGB image arises the possibility of replacing the last two bits with the bits of the encrypted message. Several procedures have been developed to hide an image within another image however in most cases the payload image has to be within the accommodatable range of the cover image and very little literature have shown methods to hide multiple images within multiple images. This paper presents a novel approach to split the image into JSON styled dictionary of key-value pairs and using a metadata graph to locate different parts and positions of the payload images in the entire cluster of cover images. The model could be easily used in the real world scenario for privately sharing secret data over public communication channels without being noticed.

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Application of bi-orthogonal wavelet transform and genetic algorithm in image steganography

Cited by 48 publications

References 18 publications

A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

Images Within Images? A Multi-image Paradigm with Novel Key-Value Graph Oriented Steganography

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