A Huffman Code LSB based Image Steganography Technique Using Multi-Level Encryption and Achromatic Component of an image

doi:10.21203/rs.3.rs-2579014/v1

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A Huffman Code LSB based Image Steganography Technique Using Multi-Level Encryption and Achromatic Component of an image

https://doi.org/10.21203/rs.3.rs-2579014/v1

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published 30 Aug, 2023

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It is essential in the field of cover steganography to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, robust, and working on the visual quality is the essential factors of this research to make a reliable mechanism. We can’t compromise on image quality up to a confident flat because it halts the concepts of cover steganography while the maximum embedding limit is also the main factor that makes the technique more efficient. So, Image steganography is the state of art method that hides a data inside any cover mediums such as images, videos, texts, audios etc. Steganography is the specialty of implanting a mystery message so that nobody can think of it or no unaided eye can identify it. There is no information on the current information inside the cover object in the wake of encrypting. In the recent couple of years, due to the achievement of accelerated popularity of the internet, various organizations such as government offices, military, private companies etc. use different transferring methods for exchanging their information. The internet has various benefits and a few demerits. The primary bad mark is protection and security and information transmission over an unreliable network. Different cover steganography research strategies have been recommended as of late yet, and each adores its benefits and impediments but, there is the need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, this paper proposed a future method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concept, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The suggested method used an encrypted algorithm to upheld the appearances of the image. The algorithm also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM) etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection.

Biological sciences/Computational biology and bioinformatics/Data processing

Biological sciences/Computational biology and bioinformatics/Image processing

Biological sciences/Computational biology and bioinformatics/Software

Currently, due to the accelerated popularity, inexpensiveness, and efficiency of the internet, it has become an excellent system for data transmission [1]. Therefore, the wide sharing of information in digital form as one file image is used and it becomes very easy to send over the internet [2]. In addition, it is very easy to copy or modify the transmitted data over internet by any unauthorized persons or attackers. There are various identified tools available to make the exploitation of secret information, security, privacy etc. being transmitted and also to make the possibility of different vulnerabilities, attacks, hateful threats such are scaling, cropping, tempering, spoofing, phishing, eavesdropping, privilege escalation, clickjacking, social engineering, bot, backdoor, viruses, botnet, malwares and many more [3, 4]. Various techniques are proposed in the recent couple of years to handle secure communication but each has is related pros and cons. So, to fulfil the need of secure communication, it is necessary to find some better ways to make a secure system to fulfil the need of the transmission over internet between users. However, the ancient method used for secure communication to provides a safe way of transferring data over internet is Encryption [5, 6] [7]. It converts the normal text mean plain text to cipher text using some keys. So Encryption is a way of converting a plain text into cipher text within any cover media used for encryption, that there is no clue of the existing data and no one can suspect the encrypted information. Furthermore, the recipient might involve a mystery key for encryption and just the key supervisors can unscramble the mystery message utilizing the key which is to be given by the source. Using the encryption concepts there are some multimedia information mediums used such are image, text, audio, video, network etc. [8].

Therefore, the main idea of encryption is the idea of steganography which hides secret information within the cover objects without knowing the existing of the information within it. Steganography is the part of data concealing that encodes the mystery message so that nobody can identify it. Image steganography shows an essential part in protected communication in this digital world because one file type is rapidly used is image [9]. Furthermore, image steganography uses image as cover object and inserts the secret information using different reported methods such as spatial domain, frequency domains etc. The extensive surveys of the image steganography are presented in [10–14]. Every existing method has their advantages and disadvantages in terms of payload, security, perception, temper protection, and computation which are the basic criteria of steganography as well as the basic needs for any steganographic methods shown in Fig. 1 [15].

However, we have proposed an improved and novel technique based on Multi-Level Encryption (MLE) and achromatic component of an image, and Huffman LSB. Proposed method also used some transposition, magic matrix concepts to expand the significance and inspiration of the proposed technique. For embedding the secret message, the proposed algorithm used the Huffman coding priority concept and made the Huffman encrypted sequences and then embedded into the cover media. After making the Huffman encrypted sequences, different encrypted operations applied on it made the method outperform others. This is because the proposed algorithm used an embedded procedure of the cover data within image in a manner, if anyone suspect the stego image or extract the message, then they will not extract the encoded message version. Only the holders of the Huffman table or tree can regain the secret message correctly because the communication is between the two parties. Therefore, the proposed research work has a novel and improved contribution in terms of security, transparency, payload, and temper protection to make it significance. Some main objectives and contributions of the said method are given.

Proposed technique used Huffman coder priority for encrypting the secret message and the result is the form of Huffman encrypted sequences to improve the reliability between basic criteria such as temper protection and security.
Achromatic components of an image Hue Saturation Intensity (HSI) variety model are utilized rather than the RGB pictures variety model to decrease the handling time and increment the security.
For making deciphering of the mystery message testing, and thought-provoking, the proposed method divided the I channel or plane (I-Plane) into four equal blocks and shuffled the blocks using Magic Matrix, and the method also used MLE for giving the tough time to attackers and to increases the security.
The proposed strategy was fundamentally dissected in opinion of various key view-points (i.e. different size of image with different size of text, different image with same text size, different format images) to work on the proficiency and viability of the scheme.

The basic criterion of cover steganography presented in Fig. 1, which elaborates the payload maximum amount of furtive data to be implanted within the cover object. Transparency show the quality of the image. Robustness spectacles the retreat of the stego image, that the stego object is undamaged after embedding even if attacked. shows the opposition against various assaults of the cover object. While computation explain the time intricacy of the implanting system into cover object [16]. So the rest of the paper is organized in sub sections; Section 2 elaborates the abstract details of the related reported research works, Section 3 explains the proposed methodology, Section 4 and 5 conclude the experimental results and conclusion of the proposed algorithm respectively.

For secure communication between sender and receiver in this digital world over internet, cover steganography plays a vital role and flourishing research area. For digital steganography, many research works have been proposed over the last decades such as LSB, pixel value differencing, randomization, cyclic, etc. and each has their related pros and cons. Notably, for inserting the mystery message inside the cover media the most well-known and habitually utilized strategy is the LSB technique [17]. The wide uses of this technique is due to its simplicity and straightforwardness. Therefore, this section elaborates the basic concepts of RGB and HSI color model, Least Significant Bit (LSB) and critically analysis of some methods presented in literature [18].

However, how about we make sense of RGB and HSI variety models; one of the main parts of any item is its tone.

The utilization of variety in picture handling is persuaded by standard two aspects.

From a scene, that often streamlines object identification and abstraction, a color is a powerful descriptor.
Contrasted with about just too many shades of gray the people can discern thousands of color shades and intensities.

The second is predominantly significant in physical image analysis (i.e. when performed by people). The purpose of color model or color system is to facilitate the specification of colors in some standard, and generally accepted way, when working in image processing because it the main part of it. Each color tone is addressed by a solitary point, because color mode is a description of a coordinate system and subspace within that system. In practice most commonly used color models; Red, Green, Blue (RGB) model used for monitors and a broad class of color video cameras because it is the hardware-oriented models in terms of digital image processing. For printing the second color model used is Cyan, Magenta, Yellow and Black (CMYK), Cyan, Magenta, Yellow (CMY). To corresponds closely with the way humans, describe and interpret color, the third color model Hue, Saturation, Intensity (HSI) is used. So, the main focus of this study is HSI model. In HSI color Model, changing from one model to the other is an open process while creating colors in the RGB and CMY models [19]. For hardware implementations, ideally suited is this color system. The RGB framework coordinates pleasantly with the way that the natural eye is firmly viewpoint to red, green, and blue primaries. Yet, the RGB, CMY, and other comparable variety models are not appropriate for depicting colors in wording that are pragmatic for human translation.

For instance, one doesn't allude to the shade of color by giving the level of every one of the primaries creating its tone. Besides, we don't consider a variety of pictures as being made out of three essential pictures that join to frame that solitary picture [20]. So, we describe by its hue, saturation, and brightness, when human view a color object. Hue is a color aspect that defines a clean color (i.e. Yellow, red, and orange), while saturation gives a measure of the degree to which a pure color is diluted by a white light. Whereas intensity is a particular descriptor that is basically difficult to gauge. It is one of the key aspects in describing the color sensation, and it also exemplifies the colorless thought of intensity. This quantity absolutely is measurable and simply interpretable because it is an utmost convenient descriptor of monochromatic images. In a color image, the HSI model decouples the intensity component from the color carrying information (hue and saturation). Based on color descriptions HSI color model is an ideal tool for developing image processing algorithms because that are naturally intuitive to humans. For color generation, we can say that RGB is ideal tool, but for color description it is significantly more restricted. Hereafter, from in RGB image we should be capable to excerpt intensity [21–22]. However, HSI color model plays a vital role in secret information camouflage because Intensity plane (I Plane) does not grief the other planes, unique RGB diverts in which all planes are unequivocally co-related with one another. Besides, handling an image in HSI model is somewhat more economical based on LSB based technique etc. So, LSB is the process of embedding the secret message into cover image pixels of LSB’s either randomly or sequentially shown in Fig. 2 [23–25]. The given figure explores the concept of LSB by taking the cover image converting into their corresponding American Standard Code Information Interchange (ASCII) values then converting into binary values [26].

After reading the image pixels’ values and its binary forms it converts the secret message values to binary form [27]. Furthermore, it the procedures of implanting secret data bit K replace with cover image pixels K LSB’s. Eq. 1 represents the embedding process.

S^IM _{(i, j)} = C^I _{(i, j) −} C^I _{(i, j)} mod ₂^K +S^M _{(i, j) Eq.1}

Where S^IM (i, j) presents the stego image i, j represents row and column, C^I is cover image and S^M secret message. Different LSB based methods are proposed recently but that still have issues to improve this research area. Human eye is very naked and having properties to detect or suspect little change in any smooth area of the images [28]. So, encrypting the secret message with the image either randomly or sequentially and not all pixel values is used for embedding so the change may be happening on only the embedding area of the image. Therefore, various image steganographic reported works are adapted to embeds suitable amount of message in appropriate cover object [29].

Lee et al. recommended a high implanting message based image steganography where they embedded 12 KB cover information inside the cover image and the technique focused only on payload [30]. Zang and Wang et al. introduced proficient steganographic inserting by taking advantage of the alteration course. The author focused on embedding more message also try to get security [31].

Khan et al. offered a cyclic image based steganography method using randomization. In this paper authors struggled to get the consistency among the basic criteria and embedded 4 to 8 KB secret message. Karim suggested new image steganography technique in light of LSB using secret key and achieving the security, payload but broke down the other criteria [32].

Muhammad and Sajjad proposed and magic LSB method using multi-level encryption and HSI image. It is a better technique in image steganography but having some limitation to different attacks such as scaling, noising, cropping etc. [33

Rustad et al developed a novel image steganography for improving the perceptual transparency of the image. It is an inverted LSB method using adaptive pattern. This research achieved a high quality stego image but the payload limit is too low [34].

Cheng, X., & Huang proposed a novel method of reversible encrypted image based steganography using interpolation image and histogram shifting. In this study the author used double scrambles operation on pixel of the image by changing the positions of the pixels. The experimental results achieved a high embedding capacity and security [35].

Shwe Sin et al. presents a LSB and Huffman code based steganography. The investigational outcomes presented that the algorithm got high embedding capacity and better security [36].

Tsai et al presented another lsb based image steganography algorithm in light of MSB prediction and Huffman base method. The main objectives of the paper are high payload and secrecy and increase the inserting pace of the mystery message inside the cover picture [37].

M. Shahu et al. present a novel technique, LBP-based reversible information stowing away effectively accomplishes better HC, SI quality, and strength to different assaults. Be that as it may, besides these benefits, the proposed method can be improved concerning HC. Since the proposed work utilizes the LBP-based strategy, subsequently, it considers the surface and smooth pictures as unclear while embedding the EBs. Regardless, the pixel power of surface pictures makes it more sensible to introduce more EBs when appeared differently in relation to smooth pictures. Thusly, the proposed work can be contacted to achieve higher HC in surface pictures by brushing LBP with the PVDS-based technique [38].

A.K. Sahu et al proposed a better method for two delicate watermarking plans to perform altering recognition and limitation in a picture. The proposed plans were outwardly disabled and the watermark pieces were created using the turbulent system based determined map at both the source and getting end. Further, the chief contrives saw a constraint of ± 1 contrast between the host and watermarked pixels. As such, the idea of the watermarked picture was far superior to that of various plans. Finally, surprising results were achieved in regards to the adjusted area and limitation limit of the proposed plans. Later on, the proposed work can be connected with self-recovery of the modified pieces, and growing both spatial and change spaces to extra overhaul the force [39]. There are different reported works presented in the literature based on image steganography, and each method has its related advantages and disadvantages depending on its embedding procedures and selection of the cover object [40]. Moreover, every examination work has attempted to cover the rudiments standards of picture steganography such are payload, strength, discernment, temper insurance, and computation. Some examination of existing techniques is introduced in Table 1.

Table 1

Critical analysis of various existing method using criterion of image steganography
Techniques	Pros	Cons	Measuring Algorithm
Techniques	Pros	Cons	Capacity	Security	Transparency	Temper Protection	Computation
Canny edge detector [41]	High Security and resistance	Low quality and payload	No	Yes	No	Yes	Yes
Huffman Encoding [42]	high capacity and a good invisibility	secure less	Yes	No	Yes	Yes	No
Huffman coding and the LSB replacement [43]	embedding capacity, security and imperceptible	Can’t resist against attacks and time consuming	Yes	Yes	Yes	No	No
adaptive Huffman code mapping (AHCM) [44]	Higher secure payload	Low quality image	Yes	Yes	No	No	Yes
AES–Huffman Coding–DWT [45]	good stego image quality and security	Can’t resist against attacks and time consuming	Yes	Yes	Yes	No	No
Enhanced Huffman-PSO based image optimization algorithm [46]	Payload, and quality images	Secure less and time consuming	Yes	No	Yes	No	No
P single/P double and Huffman Coding [47]	imperceptible, and robust	Time consuming and can’t resist	Yes	Yes	Yes	No	No
IS method based on pixels variance, eight neighbors [48]	Capacity, and securable	Low quality images	Yes	Yes	No	Yes	No
deflate compression for image steganography [49]	imperceptible, and robust	Time consuming and can’t resist	Yes	Yes	Yes	No	No
image steganography using pixel allocation and random function techniques [50]	security and imperceptibility	Low payload and time consuming	No	Yes	Yes	Yes	No
Unlimited Secret Text Size IS [51]	Payload, and quality images	Secure less and time consuming	Yes	No	Yes	No	No
HC, minimum distortion based on distinction grade value [52]	high embedding capacity, security and visual quality	Can’t resist against attacks and time consuming	Yes	Yes	Yes	No	No
Reversible data hiding with adaptive Huffman code [53]	greater embedding rate and improved security.	Low quality and consuming	Yes	Yes	No	Yes	No
fragile watermarking based on Huffman [54]	increasing safety and security	Low payload limit and low quality, consuming	No	Yes	No	Yes	No
Reversible, Time-Varying Huffman Coding Table [55]	Security, temper protection	Low payload limit and low quality	No	Yes	No	Yes	Yes
Using RSA Algorithm [56]	Security, temper protection, quality	Payload, and time consuming	No	Yes	Yes	Yes	No

In sum-up, Table 1expounded the analysis of the diverse existing methods using the basic criterion of steganography. And also expounded on the techniques used, the pros and cons, the main focus, the embedding procedure, and the limitations of each method. Though, after a detailed analysis of the existing methods many points come to mind, but some point is very vital; one is selecting an appropriate cover object and the second is the reliability between the essential criterion of steganography. Because some tried for making a reliable method but get one or two parameters but broke down the other criteria and also the reliability between the criterion which is an essential part of steganography. However, to tackle these vital needs, the proposed algorithm is designed in a manner that shows that which dimension of the image is better for which size of secret message and also to make a reliable method. However, the detail whole process of proposed algorithm is presented in next section.

This section presents the whole process of proposed algorithm, embedding and extraction process, Magic matrix, MLEA, Huffman encryption and extraction process shown in Fig. 3. The following notations are used throughout the paper presented in Table 2.

Table 2

Notations used in Proposed Algorithm
Notations/Terminology Description
C^I Represent the cover image with C^I (i, j), where i, j shows the value locations of the cover image. Cover image for secret information embedding. I^T I^T represent the transposed imager of the cover image C^I. S^D Secret Data or Message with value location denoted with S^D _{(i, j)} where i, j value location. HSI^IM Represent the converted Hue Saturation Intensity (HIS) image from RGB cover image. M_gM_tx Magic matrix a special type of MATLAB function used. Plane^H Hue chromatic components of HSI image HSI^IM. Plane^S Saturation chromatic components of HSI image HSI^IM. Plane^I Achromatic components of HSI image HSI^IM. B^{C 1,2,3,4} Represent the division of four equal blocks of the Plane^I of the HSI^IM. H^FC Huffman embedded message denoted by H^FC with H^FC _i, which representing the 8bits location of H^FC _i, of the cover message. S^D’ Recovered Secret Data or Message with value location denoted with S^D’ (i, j) where i, j value location. H^FC’ Huffman embedded message denoted by H^FC’ with H^FC’ _i, which representing the 8bits of H^FC’ _{i, j} of the cover message. H^FCS Huffman code sequences S^I Stego image denoted by S^I with the location values i, j, also called embedded image.

The step by step procedures of embedding, extraction process, MLEA, Magic Matrix (M_gM_tx), and Huffman code algorithms are presented given in Algorithm 2, 3, 4, and 5 respectively. The proposed algorithm used two stages in which first we encrypt the mystery message using Huffman code priority. For mapping the one secret word to one code word the optimal codes are Huffman code mapping. Since Huffman designates a parallel worth or code to each brightness of the cover picture. For applying the Huffman code, first we convert the 2D image with size C X R to 1D array bits’ stream with length of L_Huff fewer than or equal to C X R (L_Huff < = C x R). The given bit’s stream is used for building the Huffman table or sequences H^FCS because extracting the original message bits’ stream is totally depended on the same Huffman building table bits’ stream. To lessen the size of the cover picture performs lossless pressure of the Huffman inserting is utilized. After the size of that image is reduced because Huffman encrypting shrinkages the image size. In the context of embedding, Huffman encrypted bit stream cannot reveal or show the message bits of the cover image or anything about it. Huffman is one type of verification, because the secret message bit stream is only extracted using the same Huffman table or sequences which are used for embedding. If a little bit error occurs the Huffman table or sequences can’t extract and is unable to recover the secret message. The proposed method used MLEA, Magic Matrix, and Huffman code based encryption to prove the efficiency, effectiveness and dominancy of the proposed. Let’s first elaborates the important concepts used in proposed algorithm.

Multi-Level Encryption (MLE) is used for proposed algorithm having different encryption operations to improve the security and give hardness to the attackers [57–58]. It is used on secret data before embedding to the cover object or image for increasing security using some XOR operation to make the message unrecoverable for any assailants. While Magic matrix is used to make a matrix having no repeated numbers and the summation of that matrix either each rows, each column, and both diagonal are remain same. It can be used for applying different operation on secret data or cover image due to its properties in terms of to improve more security and also give the hard-hitting time to attackers shown in Figs. 4–8 sequentially [59–61]. Let’s try to elaborates with the help of example given below.

However, Magic matrix is special type of MALTAB function used for applying different shuffling, rotation, or reflecting properties. The above example in Figs. 4–8 respectively show us the detail explanation of the matrix by taking the magic matrix 3 by 3 and the required magic sum is 15. Now perform different calculations on the matrix to make the matrix magic. Either trying from the center point, starting left-right corners, or even-odd numbers to make the matrix magic. So take the value in terms of 3 by 3 and make the summation diagonally, each row and column, and check the properties of the magic matric, if found correct then the matrix is said to be magic shown in Figs. 5, and 6.

In the above example from steps 1–6 elaborates the step by steps method that how to making the matrix magic. You can see in the above Fig. 6, taking the square center to be 5 but the center involved 4 different sums, and if we take even numbers from the corners then each corners involved 3 different sums shown in step 4. And if we take the edges to be odd numbers then in each sums involved 2 different sums. Now try to with some other numbers; suppose can we put 1 in 4 spots, or put 3 in 2 spots, so if we 2 multiply 4 then 8 possibilities occur. So there are 8 possibilities to put odd numbers on edges, because we tried with different ways but the result is not near to the magic matrix properties but with odd numbers the different sums is low that why we take the odd edges shown in step 5. Finally, the even numbers placed at corners and the odd numbers placed on edges.

The section demonstrate the investigational results based on different perspectives, some statistical assessment matrices to demonstrate the effectiveness and enactment of the planned work. For analysis we used some standard images namely, Lena, Mandrill, Girl etc. for the analysis of the method shown in Fig. 9. It is also critically analyzed in relations of safety and enactment analysis based on three distinct perspectives shown in Fig. 10.

Before going to the performance and security analysis of the proposed algorithm, we first explain the Quality assessment metrics used for critically analysis of the proposed algorithm shown in Table 3 [62–64].

Table 3

QAM’s for proposed algorithm [62–64]
Quality Assessment Metrics
Mean Square Error MSE	The MSE metric is to differentiate and compare both stego and cover images; and as a results these both images are assumed to be equal and same in case assuming the accomplished incentive for the MSE metric is equivalent to zero. Therefore, the Mean Square Error should be less as possible. It will be considered a robust and quality image. The metric is mathematically expressed as given below. MSE=\(\text{M}\text{S}\text{E}=\frac{1}{\text{M}\text{N}} \sum _{\text{x}=1}^{\text{M}}\sum _{\text{y}=1}^{\text{N}}\left({\text{S}}_{\text{x}\text{y}}-{\text{C}}_{\text{x}\text{y}}\right)\)
Normalize Cross Correlation NCC	The Normalize Cross Correlation (NCC) metric is utilized to explore and look at how both the cover and stego images medias are same and identical. Subsequently, the two pictures are supposed to be something very similar assuming that the worth of the NCC metric is equivalent to 1 while assuming the NCC esteem for both of the images become closer to 0 then this shows the absolute dissimilarity. The formula used to compute the NCC metric is given below. \(\text{N}\text{C}\text{C}= \frac{{\sum }_{\text{x}=1}^{\text{M}}\sum _{\text{y}=1}^{\text{N}}\left(\text{S}\left(\text{x},\text{y}\right)\text{*}\text{C}\left(\text{x},\text{y}\right)\right)}{\sum _{\text{x}=1}^{\text{M}}\sum _{\text{y}=1}^{\text{N}}{\text{S}\left(\text{x},\text{y}\right)}^{2}}\)
Peak Signal to Noise Ratio PSNR	The PSNR assessment metric is the essential boundary for deciding the nature of the both stego and cover pictures. We can finish up, on the off chance that the worth of PSNR is more prominent than 30dB, the two pictures are considered quality pictures; as well as the other way around. The accompanying equation can be utilized to figure out the PNSR esteem. \(\text{P}\text{S}\text{N}\text{R}=10{\text{l}\text{o}\text{g}}_{10}\left(\frac{{\text{C}}_{{\text{max} }^{2}}}{\text{M}\text{S}\text{E}}\right)\)
Quality Index QI	The Quality Index (QI) metric is utilized to quantify the nature of the stego picture. range of 1 to -1, both images can be considering a quality images if the value is equal to 1 otherwise the image show dissimilarity between both images if the value lies in the series of -1 means less than 1. Q. in the given formula where T and H shows the both images and n is the number of the pixels in an image. The following expressions are used to compute the QI metric. \({Q}=\frac{4{\sigma }_{HT}H{\prime }T{\prime }}{\left({\sigma }_{H+}^{2}{\sigma }_{T}^{2}\right)({H}^{{\prime }2}+{T}^{{\prime }2})}\) \({\sigma }_{H}^{2}=\frac{1}{N-1}\sum _{i=1}^{N}{\left({H}_{i}-{H}^{{\prime }}\right)}^{2}\) \({H}^{{\prime }}=\frac{1}{N}\sum _{i=1}^{N}{H}_{i}-T=\frac{1}{N}\sum _{i=1}^{N}{T}_{i}\) \({\sigma }_{H}^{2}=\frac{1}{N-1}\sum _{i=1}^{N}{\left({T}_{i}-{T}^{{\prime }}\right)}^{2}\)
Correlation Coefficient CC	To find and linearity (extent & direction) of two random variables Correlation Coefficient (CC) play vital due to its properties. Both variables are said to same or closely related if the values of CC equal to 1 otherwise shows the difference if the values of both become 0. \(I=\frac{\sum _{i}\left({x}_{i}-{x}_{m}\right)\left({y}_{i}-{y}_{m}\right)}{\sum _{i}\sqrt{\sum _{i}{\left({x}_{i}-{x}_{m}\right)}^{2}}\sqrt{\sum _{i}{\left({y}_{i}-{y}_{m}\right)}^{2}}}\)
Structural Similarity Index SSIM	SSIM is utilized to choose the nature of both cover and stego-pictures. It utilized three sections, which will choose the nature of the picture assuming the worth is equivalent to 1, and in the event that the worth of all fragments is under 1 shows the distinction between the two pictures. \(\text{S}\text{S}\text{I}\text{M}\left(\text{X},\text{Y}\right)=\frac{\left(2{{\mu }}_{\text{x}}{{\mu }}_{\text{y}}+{\text{C}}_{1}\right)\left(2{{\sigma }}_{\text{x}\text{y}}+{\text{C}}_{2}\right)}{\left({{{\mu }}_{\text{x}}}^{2}+{{{\mu }}_{\text{y}}}^{2}+{\text{C}}_{1}\right)\left({{{\sigma }}_{\text{x}}}^{2}+{{{\sigma }}_{\text{y}}}^{2}+{\text{C}}_{2}\right)}\)
Image Fidelity IF	Image Fidelity (IF). is used for finding the image quality, it tends to be determined utilizing the given equation where P and S show the Cover and stego picture and I and j address beginning and finishing values. \(IF=1-\frac{\sum _{i,j}{\left(P\left(i,j\right)-S\left(i,j\right)\right)}^{2}}{\sum _{i,j}{\left(P\left(i,j\right)\times S\left(i,j\right)\right)}^{ }}\)

A. Perspective 1 Performance Analysis using P1 is given. Using P1 the planned work is evaluated on different images of distinct dimensions with 14 kb’s secret message. Other hand, it is also analyzed based different size of secret messages embedded into similar dimension images. So, the results demonstrate the connotation of the technique in view of PSNR values displayed in Table 4.While Table 5 show the performance of the proposed method compared with relevant existing methods using PSNR values which 6.22% outer performed.

Table 4

Investigation of the proposed algorithm based on P 1
Images	PSNR values based on (128x128, 256x256, 512x512, 1024x1024 Dimensions Image / 14 KB message size)				PSNR values based on 6, 8, 10, and 12 KB’s/ 512 x 512 image
Images	128	512	256	1024	8KB	10KB	12KB	14KB
Lena	89.32	80.32	74.32	74.32	74.21	82.31	81.21	80.91
Peppers	86.32	78.21	82.22	89.23	87.32	84.32	83.32	83.32
Mandrill	84.21	79.31	79.21	79.91	86.32	82.21	82.22	80.23
Lake	82.12	80.21	80.01	83.99	74.21	82.31	81.21	80.91
Baby	85.21	76.21	80.23	80.11	72.12	83.21	81.01	89.99
Image1	80.23	78.01	76.01	77.99	85.21	76.21	81.23	88.11
Average of 165 images	84.57	78.71	78.67	80.93	82.07	82.78	82.58	82.65

Table 5

Average Results of Proposed algorithm based on PSNR
Image	Average Results of PSNR, Compared Proposed Algorithm with existing research works
Image	KM et.al. [28]	Rustad et al. [29]	Cheng et al. [30]	Thansm et. Al. [31]	Mahdi et al [34]	Tsai et al. [32]	Proposed Algorithm
Lena	78.043	73.002	75.221	75.001	78.991	69.001	80.001
Peppers	68.321	77.9	70.009	81.988	82.221	65.001	79.001
Mandrill	71.211	79.991	81.002	81.008	73.009	71.988	83.988
Lake	63.002	75.221	75.001	68.991	72.001	79.001	81.002
Baby	77.9	70.009	71.988	75.221	77.001	75.001	78.779
Image1	79.991	80.002	81.008	70.009	75.988	82.988	83.99
Average	73.08	76.02	75.70	75.37	76.54	73.83	81.13

B. Perspective 2: Security Analysis of the Proposed algorithm based on P 2 is given. Table 6 elaborate the significance, resistance against to different attacks, quality, and performance of the propose method using different quality assessment parameters.

Table 6

Security Analysis of the proposed Algorithm based on Perspective 2
Image	Average Result of the proposed algorithm using some Security analysis parameters
Image	MSE	SSIM	NCC	CC	IF	QI
Lena	0.1	0.979	0.989	0.999	0.999		1
Peppers	0.2	1	1	0.989	1		0.998
Mandrill	0	0.98	0.998	1	1		0.998
Lake	0	1	0.997	0.978	0.998		1
Baby	0.01	1	0.99	0.999	1		1
Image1	0	0.999	1	1	1		0.999
Average	0.05	0.99	1.00	0.99	1.00		1.00

c. Perspective 3 Histogram Analysis of the proposed algorithm based on P3 is given. Histogram Examination shows the real contrast between both stego and cover images. Due to its properties, a little difference between images can be founded, because it shows the extract occurrences of the pixels of the image. Figure 11 shows the analysis of the proposed algorithm using three standard images namely, Lena, Mandrill, House as a both stego and cover images histograms.

However, the empirical results of the proposed algorithm grounded on diverse viewpoints using assessment metrics prove the improvement, efficiency and effectiveness of the method. Our method improves the payload, high level security, temper protection, and better visual quality of the image. Suppose if attackers or any naked eye suspects the technique used in proposed algorithm which is LSB and some improved technique of steganalysis. Then attacker can’t extract the actual contents of the secret message because proposed method used Huffman code, MLE, Magic matrix and HSI (achromatic components of an image). If attacker extracts the message upto some limits then the contents is useless, because for extraction of the full message text attackers needs to used Huffman code, MLE etc. for getting the actual message.

The study proposed a novel method using Huffman code, HSI color model, MLEA, Magic matrix, and LSB substitution. Embedding the secret message, the I-plane of the HSI variety model is utilized as the cover image rather than the RGB model, on the grounds that for expanding the safety and reducing extra computational upstairs or processing time. The empirical outcomes of the proposed technique secure a normal of PSNR 79.29 dB over 165 standard images and also proves the supremacy, and efficiency of the proposed method compared with some stated correlated works. We also used MLEA, Magic matrix and I-plane of the HSI image for secret message embedding which makes this algorithm dreadful and abstruse and also unclear and foolish the steganalysis process. Our proposed method also used Huffman code which make it more robust than existing methods. However, we infer that it is capable of shaping the stego picture practically identical and also has fitness to give adeptness, effectiveness and justify the encouraging demands of the current system and user to generate better quality stego images. So our method is easy to programed, simple and better combination in terms of transparency and robustness. The results from different perspectives show the achievability and outperforming of our method to others. But still needs some reasonable improvements by functioning on magic matrix and MLEA extension to make the technique more dependable and also implementation of the method into transform domain. We are also now involved in outcome further such ways as unsupervised learning (ML), related concepts of Deep Learning to grab some boundaries, statistical and image processing assaults to produce excellent and dependable free stego images.

Acknowledgment

This research is supported by the University of Peshawar and the Abdul Wali Khan University Mardan (AWKUM), Pakistan.

Availability of Data and Materials

The datasets produced and/or analyzed during the current study are openly accessible in the Kaggle repository, and SIPI, and can be gotten to at [https://www.kaggle.com/datasets/mnavaidd/image-segmentation-dataset]. Further, different pictures utilized inside the exploratory work are freely accessible on the web. All the codes used for this method will be provided for research purposes if requested by researchers.

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No competing interests reported.

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A Huffman Code LSB based Image Steganography Technique Using Multi-Level Encryption and Achromatic Component of an image

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