INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
A Safe and Imperceptible Approach for hiding Encrypted Text in  
Color Images Using LSB Steganography  
Suvhodip Saha, Soumendu Banerjee  
Department of Computer Science and Engineering Academy of Technology, Hooghly, West Bengal  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000136  
Received: 06 January 2026; Accepted: 10 January 2026; Published: 17 January 2026  
ABSTRACT  
Steganography refers to various means of concealing a secret message from view in a digital environment by  
concealing its visibility so that no one sees it. There are many types of steganography; however, many prefer the  
use of images and the Least Significant Bit method as it is very simple to use and can hide a great deal of  
information within an image. In this paper, an efficient LSB-based image steganography technique is designed  
and implemented to securely hide sensitive information while preserving the visual quality of the original image.  
The secret information is placed in the least significant bit of each pixel, making the stego image  
indistinguishable from the original image presented as a cover image. Experimental evaluation using P. S. N. R  
and M. S.E ensures minimal distortion and high opacity.  
Keywords- Steganography, Least Significant Bit (LSB), Image Steganography, Mean Squared Error (MSE),  
Peak Signal-to-Noise Ratio (PSNR), Test hiding, Cyber Security  
INTRODUCTION  
Digital media (photographs, videos, etc.) allows for the concealment of data through steganography.  
Steganography can be achieved by hiding information in a digital media file or by creating an invisible message  
behind a digital media file. An example of steganography is to use another image as a cover image to hide data  
(text, binary data, etc.) and keep the visual quality of the cover image intact. In response to the rapid development  
of digital and telecommunications, as well as the growing number of security threats associated with them, there  
has also been a marked increase in the amount of attention being paid to the use of steganography within secure  
communications, copyright protection, and personal privacy through the use of data encryption [1].  
The use of steganography is not limited to digital text; image-based steganography techniques are used  
extensively. Among all of image-based techniques, the spatial domain techniques, which include the least  
significant bit (LSB) method, are popularly employed because of their simplicity, low computational complexity,  
and ability to hide large amounts of data. The LSB technique hides data by inserting bits from secret messages  
within the least significant bits of the pixel intensity value; as a result, there is a minimal amount of visual  
distortion to the cover image. To date, multiple researchers have shown that LSB-based methods are much more  
ambiguous than conversion domain techniques, such as Discrete Cosine Transform (DCT) or Discrete Wavelet  
Transform (DWT), when used to hide text data[2],[3].  
Current research has focused on improving upon the traditional LSB technique by adding encryption,  
randomization, and hybrid approaches to improve the overall security, preserving the quality of the cover images.  
These improvements introduce a trade-off between robustness and opacity, which makes objective quality  
assessment indispensable. Min Squared Error (MSE) and peak signal-to-noise ratio (PSNR) are commonly used  
to measure the distortion between the original cover image and the generated stego image. Research has shown  
that using LSB to hide encrypted text results in lower MSE and higher PSNR values than using conversion-  
domain techniques; therefore, the visual quality and strength of concealability of LSB methods compared to  
other image steganography techniques[4].  
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In response to the aforementioned needs, this paper proposes a new LSB-based image steganography method  
for hiding encrypted text in color images. The main purpose of this work is to ensure the correct extraction of  
secure embeddings and hidden text while minimizing perceptual distortion. The effectiveness of the proposed  
method is verified through experimental analysis using MSE and PSNR metrics, which shows that Stego images  
maintain high visual fidelity compared to the original cover images[5].  
MAJOR CONTRIBUTION  
1. We have developed an LSB-based image steganography technique to securely hide hidden text information  
in digital color images.  
2. The goal of developing a simple, easy-to-use app with the purpose of providing a way to automate the  
embedding and extraction process of encrypting text as well as creating a visually identical image (the Stego  
Image) to its original (the Cover Image).  
3. The proposed system enables the secure transmission of stego images over public or private communication  
networks, allowing the intended recipient to accurately extract the hidden message using the same displayed  
method.  
4. Mean squared error (MSE) for performance evaluation and peak signal-to-noise ratio (PSNR) and objective  
image quality assessment metrics including have been used, with results indicating minimal distortion and high  
opacity.  
RELATED WORK  
Least Significant Bit (LSB) based image steganography has been extensively investigated due to the inherent  
redundancy of digital images and its ability to embed secret information with minimal perceptual distortion. One  
of Chan and Cheng [6] earliest and most influential works introduces simple LSB substitution to hide  
information, showing that the least significant bits of pixel values can effectively hide information while  
preserving visual quality. The success and continued growth of LSB steganography within the spatial domain  
has established LSB as a reliable and cost-efficient approach to a wide range of Steganographic techniques and  
methods Recent studies have focused on enhancing the security and robustness of traditional LSB methods.  
Banoori et al.[7] research on hybrid image steganography using AES base-encrypted image data, combined with  
LSB embedding technique. The hybrid approach significantly enhances the security of information embedded  
using standard LSB techniques but still provides a very high PSNR and low MSE score. Stochastic LSB  
embedding methods are a significant enhancement over traditional deterministic LSB embedding methods.  
Wang et al. [8] demonstrated that rendering randomness in the selection of LSB replacement pixels creates an  
essentially random pixel in which to embed messages, significantly reducing the possibility of a successful  
statistical attack. Rahman et al.[9] presented further improvements in LSB-based steganography and proposed  
an optimized LSB substitution technique for efficient text embedding. P. S. N. R and M. S.E . Their experimental  
results through e-analysis demonstrate high invulnerability and robustness.  
Panigrahi and Padhi [10] Some researchers extended the LSB-based methods to include the ability to hide the  
information in images using specific parameters. This type of embedding allows for greater control over visual  
artifacts associated with the process of embedding. Survey-based studies have also contributed to consolidating  
existing knowledge. Chaudhary et al.[11] Provides a comprehensive review of LSB-based image steganography  
techniques, comparing capacity, safety, and opacity metrics.  
Patil and Sonaje [12] have developed a method which combines AES encryption and LSB to form a crypto-stego  
model that adds further robustness to the hybrid security approach. In addition to these methods, more recent  
research has shown a trend toward the use of intelligent machines and learning algorithms to perform  
steganography in a number of different ways (including but not limited to LSB embedding methods).Hitney et  
al.[13] Deep learning-based image steganography conducts a comparative performance evaluation of techniques,  
showing that neural networks can outperform conventional methods in complex attack scenarios.  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Raiyan and Kabir [14] proposed a secure LSB framework using randomized encryption and Reed-Solomon  
coding to increase robustness against noise and data loss. As interest grows in using Generative Adversarial  
Networks (GANs) and Artificial Intelligence (AI) for digital forensics, Rehman [15] proposed a method of  
steganography that was enhanced through use of GANs, achieving a much higher level of resistance against  
steganalysis. In addition, Suresh and Kamalakannan [16] have developed new ways of scanning the spatial  
domain for LSBs, thus allowing for greater opacity through improved pixel traversal actions. Emerging research  
has also addressed semantic accuracy and extraction reliability.  
Lee et al. [17] reported advances in semantic accuracy and reliability of the extraction of hidden text via natural  
language processing models for steganographic applications. DeSalvo [18] explored steganographic embedding  
as a data enhancement, indicating greater applicability in the context of machine learning. Recent hybrid  
encryption-based solutions continue to strengthen LSB relevance.  
Radivilova et al. [19] An LSB-AES-based image steganography method was proposed, with an emphasis on safe  
transmission and accurate extraction. In addition, Rehman [20] further demonstrated the effectiveness of GAN-  
based steganography for hiding high-capacity and invisible data.  
In conclusion, despite the emergence of advanced methods of transforming fields of data through deep learning,  
the simple operation of LSBs yields good fidelity and performance, which is further enhanced by adding  
encryption methods, randomness and intelligent optimization, providing inspiration for the secure LSB-based  
steganography approach outlined here.  
Steganography and Types  
Steganography and cryptography are both methods to conceal information. Steganography is focused on  
concealing the "presence" (of a message), while cryptography conceals the "content" (of a message). As a result,  
steganography is used in circumstances where confidentiality and security of the message are significant  
concerns. There are different ways to classify the various types of steganography based on the type of media  
(e.g., images, videos, sound, etc.) used to conceal the secret message.In this article, we discuss a number of  
common types of steganography:  
1. Text-Based Steganography: Text-based steganography is when secret information is placed within a text  
document without altering the readability of the document. Examples of text-based steganography include line-  
shift coding and word-shift coding, and character switching. Even though this is a straightforward process, text-  
based steganography does not have many capabilities and is at risk of reformatting the text or otherwise altering  
it.  
2.Image-Based Steganography: Image-based steganography is one of the most common forms of steganography,  
as secret messages may be hidden within a digital photo (or other image) by changing the value of pixels in the  
image. Pictures are suitable for hiding information due to their large size and high redundancy. The least  
significant bit (LSB) method is the most popular spatial-domain technique, where hidden bits are embedded in  
the least significant bits of the pixel intensity, resulting in minimal visual distortion.  
3.Audio Steganography: Audio Steganography is a technique that is used in order to hide information in a way  
that is not detectable to the human ear (Auditory Imaging Techniques). This is accomplished by adjusting  
samples of audio media in a fashion that is not audible by the average human being. Techniques include LSB  
substitution, phase coding, and echo hiding. This type of steganography is often used in voice communication  
systems.  
4. Video-Based Steganography: It is a process of similar to video, there is a digital medium used to embed  
information in a video as part of the Video Steganography Method. Video File Steganography involves  
embedding hidden files into digital video files.It provides higher strength and robustness due to the combination  
of image and audio components. The information may be hidden in the spatial or frequency domain of the video  
frame.  
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5. Network Steganography: Network-Based Steganography is the term used to describe the process of sending  
messages across a digital communications network. Steganography uses protocols to obfuscate data through  
modifications to the header, timing of packets, and ordering of packets to intelligently alter the appearance of  
the packets as they travel across a network.Steganography over the network is often used for securely  
communicating through a network.  
Fig.1 Steganography types  
Brief Algorithm Implementation  
Least Significant Bit (LSB) Steganography Technique  
The least significant bit (LSB) steganography technique is one of the most widely used spatial-domain methods  
for hiding secret information in digital images. In this method, the least significant bit of value of each pixel of  
a cover image is modified to embed the secret information. When the change is made in the least significant bits  
(LSB), it is impossible for humans to see any difference between the original (cover) image and the new (stego)  
image. There are a number of advantages when using LSB-based steganography, including being easy to use,  
having a high data embedding capability, and having a low computational complexity. Because of this, they are  
ideal for applications where you want to hide text securely.  
In the proposed project, the secret text is first encrypted to increase security before being embedded. The  
encrypted text is converted into a binary bit stream. Then a color cover image is selected and its pixel values are  
processed channel-wise (red, green and blue). The bits of the encrypted message are sequentially inserted into  
the least significant bit of the pixel value. This continues until the entire message has been embedded into the  
new image. The new image should look very similar to the original cover image. When you want to take out the  
message, you look at the new image and process it exactly the same way that you inserted the bits. You then  
take all the bits that you extracted from the pixel values and combine them to reconstruct the encrypted message  
and decrypt it to get the original text. The effectiveness of the proposed method is measured by the mean squared  
error (M. S. E) and peak signal-to-noise ratio (P. S. N. R) is evaluated using metrics, which ensures minimal  
distortion between the cover and the stego images. The functional strategy of this algorithm is shown in Figure  
2.  
Fig 2 System architecture digram  
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Table 1  
Pseudocode for Encoding Process  
1. Encrypt secret _ text using encryption _ key  
2. Convert encrypted_text into Binary Bit Stream  
3. Read cover_image and extract pixel value  
4. Initialize message Index = 0  
5. For each pixel of the cover _ image:  
For each color channel (R, G, B):  
If the message index < length of binary message:  
Replace LSB of pixel value with current message bit Increase message index  
6. Save modified pixels as stego _ image  
7. Return stego_image  
Table 2  
Pseudocode for Decoding Process  
1.Read Stego_images and extract pixel values  
2. Initialize empty bit stream  
3. For each pixel of Stego _ Image:  
For each color channel (R, G, B):  
Extract pixel quality LSB Append extracted bits to the bit stream  
4. Convert bit stream to encrypted text  
5. Decrypt encrypted text using encryption _ key  
6. Return original secret_text  
Fig 3 Cover Image  
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The cover image refers to the original digital color image chosen to embed the hidden text. In the proposed  
system, the cover image acts as a carrier medium that conceals confidential information without arousing  
suspicion. Using a lossless image format prevents the loss of data due to compression so that the pixel data  
remains unchanged after embedding the secret letter within the pixel values of the cover image. Each pixel of  
the cover image is represented by three color components—Red, Green, and Blue (RGB)—with each component  
consisting of 8 bits, resulting in a total of 24 bits per pixel.  
In the proposed LSB-based image steganography procedure encodes concealed information into a full-colour,  
RGB (Red, Green, Blue) image by altering the lowest significant bit (LSB) of the pixel's intensity value (i.e., an  
LSB is one of the 8 bits representing each colour channel). A single pixel in 24-bit colour RGB images consists  
of three colour channels (RGB), with 8-bits allocated to each colour channel for a total of 24 bits in a given pixel.  
Only the last bits of selected colour channels of a pixel are modified to conceal information, allowing visual  
distortion to be fully concealed from normal human perception.  
To illustrate the process of bit-exchange, consider three consecutive pixels from the cover image presented in  
binary form:  
(01,100,111 11,100,001 11,001,100)  
(00,101,111 11,000,000 10,101,001)  
(11,010,000 00,100,001 11,111,001)  
All 8-bit groups represent different color elements. The secret character ‘A’ has an ASCII value of 65, which in  
binary is:  
A = 01000001  
These 8 bits are sequentially embedded in the LSB of the RGB elements of the pixel. When embedding, only  
the last bit of each color value is replaced with a slightly hidden message, while all higher-order bits remain  
unchanged. After embedding the binary sequence of 'A', the modified pixel matrix becomes:  
(01,100,111 11,100,000 11,001,100)  
(00,101,110 11,000,000 10,101,000)  
(11,010,000 00,100,001 11,111,000)  
The only bits that change are those of the Least Significant Bit (LSB), thus providing a relatively small fraction  
of possible bit changes; less than half of 72 bits (due to 3  
pixels at 24 bits per pixel). The final LSB set to `0` in colour component 3 of the third pixel is the marker that  
indicates the end of the hidden message or information, allowing proper recovery to take place when decoding.  
Fig 4 Stego image  
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During the embedding phase, the cover image is converted into a pixel matrix and the least significant bits of  
the pixel intensity values are targeted for change. By changing the Least Significant Bits, the new cover image  
appears virtually identical to the original cover image. The LSB technique creates a cover image that cannot be  
seen by the human eye. Thus, it protects the confidentiality of information that is being hidden.  
Use case diagram  
The use case diagram represents the functional workflow of the projected LSB-based text steganography  
application is illustrated in the use case diagrams, detailing how end-users interact with the LSB-based text  
steganography to securely send hiden text using a digital image or vice versa. The end-users are typically defined  
as two main roles: the ability to send and receive. The process of sending text starts with selecting a cover  
(carrier) image for embedding. The sender then enters the secret text message that needs to be sent securely.  
Before embedding, the system encrypts the secret text to increase privacy and prevent unauthorized access, even  
if an attempt is made to extract. Once the encryption is complete, the system implements the Least Significant  
Bit (LSB) embedding technique, where encrypted text bits are inserted into the least significant bits of the pixel  
value of the cover image. The alteration from the original image to the stego captures little visual distortion.  
Therefore the resulting stego retains enough elements of the original image, making it indistinguishable from  
the original image visually. A newly created image containing the inteded hidden text will then create a new  
stego image, which is then sent from the sender to the such that they can then receive and enter the stego image  
into their own system. Thereafter, the system processes the stego image, utilizing the Least Significant Byte  
(LSB) of the pixel data, in the same sequence as the secret text embedded. Once the encrypted data has been  
extracted, the data is decrypted by the system using the appropriate key to obtain the original secret text message.  
The use case diagram depicts how both encryption and LSB embedding provide the means for invisible methods  
for transferring data securely and reliably extracting that embedded data. The image also reflects the system's  
ability to support secure communication over open networks while maintaining image quality, which is then  
validated using performance metrics such as MSE and PSNR.  
Fig5 Use Case Diagram  
The decryption process is done on the side of the receiver to recover the original secret text from the stego image.  
Detailed steps to be able to extract a secret message will begin with the receipt of the finalized stego image.The  
first step in the decoding process is to retrieve the binary data from the least significant bit (LSB) of each pixel.To  
extract the binary data for the decoding process, each pixel's least significant bit (LSB) will be used. After  
encoding a secret message in encrypted format, this data was then embedded inside the photo. The binary data  
retrieved from the LSB of the pixel, just like the secret message, will also have to go through an appropriate  
decoding process before being decoded. The binary data that is decoded will become plaintext (readable) when  
it passes through the correct code-decoder. This ensures that regardless of whether someone obtains the encoded  
binary data who doesn't have access to the correct decoding key, the secret message remains secret; no one will  
be able to read the encoded binary data unless they possess the correct decoding key.The combination of  
steganography and cryptography is a more completely secure method of communicating.  
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Table 3  
Image Name  
Image Type  
MSE  
PSNR (dB)  
Quality Assessment  
Horse.jpg  
RGB image  
0.0000368  
92.47  
Outstanding  
Pseudo code for decryption  
1. Load the stego image with embedded data.  
2. Create an empty string for all the binary data to extract.  
3. Loop through all of the pixels in the stego image sequentially.  
4. Retrieve each pixel's LSB.  
5. Add each retrieved LSB to the Binary Data String.  
6. Break the Binary Data String into 8-bit segments.  
7. For every set of 8 bits segments in binary, convert it to ASCII.  
8. Store the result as encrypted text.  
9. Use the decryption method that corresponds with the secret key to decrypt the message.  
10. Change the decrypted data from binary back to readable text.  
11. Display or store the retrieved secret message.  
12. End process.  
Experimental result  
MSE measures the average square difference between the pixel value of the cover image and the stego image,  
and PSNR measures the quality of the stego image compared to the original cover image.  
The formula of the Mean Squared Error (MSE) is  
1
푀
푖=1  
푁
(I(i,j)−K(i,j)2  
∑
∑
푗=1  
MSE=  
푀푁  
The formula of the Pick signal to Noise Ratio is  
2
PSNR= 10log10((255)  
)
푀퐴푋  
A lower M. S. E value indicates that the distortion introduced during data embedding is minimal. The higher P.  
S. N. R values indicate better understanding and higher visual similarity between the images.  
Table 5. Entropies of Cover and Stego images  
Name of the image file  
Entropy of Cover image  
Entropy of Stego image  
Horse  
7.4711  
7.4600  
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An MSE measure of 0.0000368 suggests that there is minimal distortion between the cover image and the stego  
image as this value is very low. Further, a PSNR of 92.47 dB indicates that the quality of a Stego Image is nearly  
equivalent to that of the original Image, which is also referred to as the Cover image. Using the LSB technology  
(Least Significant Bit) to hide data in text-based Steganography will produce a very high success rate with  
respect to securely hiding sensitive information while keeping it safe without anyone ever knowing that it was  
inserted into the data.  
FUTURE DIRECTION  
The field of image steganography continues to provide vast opportunities for innovation and practical  
applications in secure communications. Future research may focus on developing more powerful and intelligent  
algorithms that improve both the ability and ambiguity of hidden information. Advanced techniques such as  
adaptive LSB methods, transform domain embedding (DCT, DWT), and hybrid methods can make detection by  
steganalysis significantly harder, thereby increasing security. The integration of machine learning and artificial  
intelligence can allow for dynamic selection of optimal embedding locations, thus creating hidden data that will  
be better protected from common image processing activities (e.g., compression, filtering, and resizing).  
Furthermore, the usage of steganography with cryptographic techniques will produce an even higher level of  
security because the information will remain indiscernible even when detected without using the appropriate  
decryption key. As the volume of digital media and content on the internet continues to grow at an exponential  
rate, the potential applications of steganography are now being extended into web-based applications, social  
media sites, and cloud storage. Therefore, secure data transmission can occur instantaneously in such settings  
too. The authors also recommended that researchers explore the possibility of using multi-modal types of  
steganography, meaning embedding information in multiple formats or media (such as text, image, audio)  
simultaneously to provide more variety and security for transmitted messages. Furthermore, future work will be  
directed toward stealth optimisation that seeks to minimise the overall statistical footprint of all hidden data and  
therefore challenges advanced steganalysis systems. Steganography can also be embedded into IoT networks  
and smart devices to facilitate safe and private communication in otherwise secure environments, such as those  
found in defence, healthcare and critical infrastructure sectors. Therefore, the ongoing development of image  
steganography will provide a significant contribution to the fields of Cybersecurity, Digital Rights Management,  
and Exchange of Confidential Information, making it one of the most important areas being researched and  
developed in today's rapidly changing Digital World.  
CONCLUSION  
This paper demonstrates a safe and efficient technique that uses LSB-based steganography to hide messages in  
color images. By embedding encrypted secret text in the least significant bits of the pixel values, the proposed  
system successfully achieves high opacity while maintaining the visual quality of the Stego image. Experimental  
evaluation using objective image quality metrics, such as mean squared error (MSE) and peak signal-to-noise  
ratio (P. S. N. R) shows that the distortion introduced by the embedding process is minimal, which is very low  
M. S. E-value and similarly high P. S. N. R and defined by value. This confirms that the primary objective of  
this project, to create a stego image that is visually indistinguishable from its cover image, has been  
accomplished. Furthermore, by adding an encryption layer to the embedding process, the security of the overall  
system is enhanced because even if someone detects the presence of hidden data before it is placed in a cover  
image, they will not be able to decipher it without having the decryption key. Due to its low computational costs,  
uncomplicated nature and ability to create the desired effect, it is possible for this method to be used in the real  
world for Secure Communication Themes (eg Confidentiality & Obfuscation). Overall, the proposed method  
provides a balanced solution between data protection, embedding capabilities, and image quality, thereby  
verifying the suitability of LSB-based steganography for secure text transmission.  
ACKNOWLEGMENT  
I would like to express my sincere gratitude to, Dr. Soumendu Banerjee, Assistant Professor in the Computer  
Science Department, who provided me with infinite advice, insight, and encouragement throughout my research.  
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I also appreciate him to contributed the development of my research and assisted me throughout my journey.  
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