LSB Algorithm Research Articles

A two-domain quantum color image watermarking scheme based on LSB algorithm

A two-domain quantum color image watermarking scheme based on LSB algorithm

Noise immunity algorithm for embedding a digital watermark in medical images

When storing and transferring printed medical materials, such as tomograms or radiographs, there is a need to protect additional information from unauthorized access. This information includes personal data of the patient and a summary of the medical history, and it can be added to the medical image (container) in the form of a watermark. Existing algorithms for embedding digital watermarks (DWM) in graphic objects distort the initial characteristics of the container image, which in the case of medical images can lead to a misdiagnosis. This study aimed to develop a distortion- and noise-resistant algorithm for embedding the DWM in the spatial domain of a medical image intended for storage on paper (as a printout). The article initially considered the possibilities of using the method of modifying the least significant bits of image pixel brightness or the LSB algorithm. Mathematical modeling in Matlab showed that the maximum brightness of the DWM guaranteeing its invisibility cannot exceed 2% of the maximum brightness of the container image for monochrome images, and 6% for color images. The maximum value of the white noise dispersion, at which it is possible to single out a DWM with a correlation coefficient of at least 0.9 was 0.0001. We prorose a new noise immunе algorithm (NIA) for embedding the DWM in the subpixels of the main image, which, after extracting the DWM, are not used to build the container image. In the absence of noise, there are no distortions of the original medical image whatsoever. The essence of the NIA is as follows. The size of the original image in the form of a two-dimensional array is quadrupled by adding a subpixel in each row and column with a brightness equal to the average arithmetic brightness of neighboring pixels. A DWM with the same size as the original image is added to the resulting subpixels. Matlab modeling showed that the DWM would remain invisible at a relative brightness of approximately 5% for monochrome container images and 15% for color images. For the NIA algorithm, the maximum value of the white noise dispersion to obtain a correlation coefficient of 0.9 is 0.005, which means that the noise immunity of the proposed method is significantly higher than that of the LSB-based algorithm.

Image steganography techniques for resisting statistical steganalysis attacks: A systematic literature review.

Information hiding in images has gained popularity. As image steganography gains relevance, techniques for detecting hidden messages have emerged. Statistical steganalysis mechanisms detect the presence of hidden secret messages in images, rendering images a prime target for cyber-attacks. Also, studies examining image steganography techniques are limited. This paper aims to fill the existing gap in extant literature on image steganography schemes capable of resisting statistical steganalysis attacks, by providing a comprehensive systematic literature review. This will ensure image steganography researchers and data protection practitioners are updated on current trends in information security assurance mechanisms. The study sampled 125 articles from ACM Digital Library, IEEE Explore, Science Direct, and Wiley. Using PRISMA, articles were synthesized and analyzed using quantitative and qualitative methods. A comprehensive discussion on image steganography techniques in terms of their robustness against well-known universal statistical steganalysis attacks including Regular-Singular (RS) and Chi-Square (X2) are provided. Trends in publication, techniques and methods, performance evaluation metrics, and security impacts were discussed. Extensive comparisons were drawn among existing techniques to evaluate their merits and limitations. It was observed that Generative Adversarial Networks dominate image steganography techniques and have become the preferred method by scholars within the domain. Artificial intelligence-powered algorithms including Machine Learning, Deep Learning, Convolutional Neural Networks, and Genetic Algorithms are recently dominating image steganography research as they enhance security. The implication is that previously preferred traditional techniques such as LSB algorithms are receiving less attention. Future Research may consider emerging technologies like blockchain technology, artificial neural networks, and biometric and facial recognition technologies to improve the robustness and security capabilities of image steganography applications.

Enhancing the Capacity and Robustness of an LSB Algorithm Using a Novel Insertion Method, Hashing Function, and Secret Key

Enhancing the Capacity and Robustness of an LSB Algorithm Using a Novel Insertion Method, Hashing Function, and Secret Key

Software implementation of image steganography based on LSB algorithm with Caesar’s cipher

The purpose of this work is to conduct a study of computer steganography methods and the implementation of image steganography using the LSD algorithm and the Caesar cipher. The development of global computer networks allows fast and efficient transfer of electronic documents, but at the same time there is a risk of illegal copying and distribution of materials. Confidentiality of important information is an extremely responsible matter. Therefore, there is a need to develop methods and software tools for hiding information in various types of files, in particular images. Steganography is a mechanism for hiding messages in cloaking objects such as images, audio, text, video, and network protocol so that the hidden message cannot be detected by the naked eye or attackers

Research on a double image security transmission algorithm of image encryption and hiding

Image encryption is an important research field. In some application scenarios, only encrypting can actually attract the attention of attackers. Therefore, this article provides dual protection for images that require confidentiality. The image is first encrypted and then hidden in the normally transmitted image to avoid attacks from attackers. The encryption adopts a combination of DNA encoding and chaos theory, while the hiding adopts LSB algorithm. The experimental results show that the algorithm has achieved good security effects, and the correlation coefficient analysis has also achieved ideal results.

Implementasi Aplikasi Steganografi Berbasis Web Menggunakan Algoritma LSB dan BPCS

Steganography is a method for concealing sensitive information in seemingly unremarkable data. In recent years, the use of steganography in web applications has become popular due to its accessibility and ability to conceal data in various types of media. Implementing a web-based steganography program that makes use of the Bit-Plane Complexity and Least Significant Bit algorithms is the aim of this project. To enable users to access the application through a browser, the system is constructed utilizing web technologies like HTML, CSS, and JavaScript during the design phase. The LSB and BPCS algorithms are employed as methods to embed secret data into user-selected images. The least significant bit of each image pixel is utilized to hold a secret piece of information using the straightforward steganography technique known as LSB. On the other hand, BPCS is a more complex steganography method that combines spatial and frequency domain analysis to hide data within high-quality images. The findings of this study show that the technique of hiding sensitive information within photos is successfully implemented by the web-based steganography program employing the LSB and BPCS algorithms.

Image Steganography Using LSB and Hybrid Encryption Algorithms

In today’s era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm’s security by augmenting the complexity of the encryption process.

Research on the Improvement of LSB-based Image Steganography Algorithm

This study has made an improvement based on the LSB algorithm, which uses a randomly generated sequence for embedding information, with an implementation environment of Python and a combination of OpenCV By analyzing the embedded data with PSNR values and pretzel-plus-noise attacks, the results show that this method has better image quality and stronger resistance to attacks, while the complexity of extracting the embedded information by external users is high. The improved LSB algorithm in this study has great reference value for information covert transmission, and can be used for image copyright protection and privacy information protection in practical applications.

Software Implementation of Modified LSB Algorithm with Shamir`s Secret Sharing

Today, it is often necessary to transmit a confidential message of a small volume, while the use of complex cryptographic systems is difficult for some reasons. One of these reasons is the impossibility of using reliable products, which, as a rule, are commercial and unavailable to the average computer user. In modern information society, many services are provided with the help of computer networks and information technologies. Information presented in digital form must be reliably protected from many threats: unauthorized access and use, destruction, forgery, leakage, violation of license agreements, disclaimer of authorship, etc. Information protection is extremely important in both commercial and government spheres. The issues of developing effective methods of protecting digital information, in particular methods of computer steganography and steganalysis, are relevant and important for the state and society. To achieve the goal, it is necessary to propose a method of increasing stego-resistance, determine the effectiveness of the created solution and analyze the obtained results. The object of research is the process of protecting information embedded in a graphic e-container. The subject of research is methods and algorithms of computer steganography and steganalysis for images. The research methods used in this work are based on steganographic algorithms.

Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

Steganography algorithms have become a significant technique for preventing illegal users from obtaining secret data. In this paper, a deep hiding/extraction algorithm has been improved (IDHEA) to hide a secret message in colour images. The proposed algorithm has been applied to enhance the payload capacity and reduce the time complexity. Modified LSB (MLSB) is based on disseminating secret data randomly on a cover-image and has been proposed to replace a number of bits per byte (Nbpb), up to 4 bits, to increase payload capacity and make it difficult to access the hiding data. The number of levels of the IDHEA algorithm has been specified randomly; each level uses a colour image, and from one level to the next, the image size is expanded, where this algorithm starts with a small size of a cover-image and increases the size of the image gradually or suddenly at the next level, according to an enlargement ratio. Lossless image compression based on the run-length encoding algorithm and Gzip has been applied to enable the size of the data that is hiding at the next level, and data encryption using the Advanced Encryption Standard algorithm (AES) has been introduced at each level to enhance the security level. Thus, the effectiveness of the proposed IDHEA algorithm has been measured at the last level, and the performance of the proposed hiding algorithm has been checked by many statistical and visual measures in terms of the embedding capacity and imperceptibility. Comparisons between the proposed approach and previous work have been implemented; it appears that the intended approach is better than the previously modified LSB algorithms, and it works against visual and statistical attacks with excellent performance achieved by using the detection error (PE). Furthermore, the results confirmed that the stego-image with high imperceptibility has reached even a payload capacity that is large and replaces twelve bits per pixel (12-bpp). Moreover, testing is confirmed in that the proposed algorithm can embed secret data efficiently with better visual quality.

LSB Algorithm to Hide Text in an Audio Signal

The following work is to hide a text in an audio file using steganography employing the LSB algorithm. 3 melodies of different genres are used to see by which of the 3, the information is best hidden, without there being any noise in the melody that will contain the hidden message. This is quite important in steganography because it is required that information goes unnoticed. To do so, we programmed in Matlab, and in this case, our hidden message contains around 9429 characters.

Enhancement of System Security by Using LSB and RSA Algorithms

A steganography hides information within other information, such as file, message, picture, or video. A cryptography is the science of converting the information from a readable form to an unreadable form for unauthorized person. The main problem in the stenographic system is embedding in cover-data without providing information that would facilitate its removal. In this research, a method for embedding data into images is suggested which employs least significant bit Steganography (LSB) and ciphering (RSA algorithm) to protect the data. System security will be enhanced by this collaboration between steganography and cryptography.

Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

Steganography algorithms have become a significant technique for preventing illegal users from obtaining secret data. In this paper, a deep hiding/extraction algorithm has been improved (IDHEA) to hide a secret message in colour images. The proposed algorithm has been applied to enhance the payload capacity and reduce the time complexity. Modified LSB (MLSB) is based on disseminating secret data randomly on a cover-image and has been proposed to replace a number of bits per byte (Nbpb), up to 4 bits, to increase payload capacity and make it difficult to access the hiding data. The number of levels of the IDHEA algorithm has been specified randomly; each level uses a colour image, and from one level to the next, the image size is expanded, where this algorithm starts with a small size of a cover-image and increases the size of the image gradually or suddenly at the next level, according to an enlargement ratio. Lossless image compression based on the run-length encoding algorithm and Gzip has been applied to enable the size of the data that is hiding at the next level, and data encryption using the Advanced Encryption Standard algorithm (AES) has been introduced at each level to enhance the security level. Thus, the effectiveness of the proposed IDHEA algorithm has been measured at the last level, and the performance of the proposed hiding algorithm has been checked by many statistical and visual measures in terms of the embedding capacity and imperceptibility. Comparisons between the proposed approach and previous work have been implemented; it appears that the intended approach is better than the previously modified LSB algorithms, and it works against visual and statistical attacks with excellent performance achieved by using the detection error (PE). Furthermore, the results confirmed that the stego-image with high imperceptibility has reached even a payload capacity that is large and replaces twelve bits per pixel (12-bpp). Moreover, testing is confirmed in that the proposed algorithm can embed secret data efficiently with better visual quality.

Increasing Message Capacity in Images Using Advanced Least Significant Bit and Image Scaling

Purpose: Steganography is the science of writing hidden or hiding messages so that apart from the sender and the recipient, no one can know or realize that a message is hidden. This paper aims to analyze the method of advanced LSB to increase message capacity. Methods/Study design/approach: The steganography technique advanced LSB algorithm develops pre-existing steganographic algorithms such as LSB by utilizing a range of media pixel values cover (images that are used as media to hide messages) with different insertion rules from LSB. Image scaling in digital image processing is known as resampling. Resampling is a mathematical technique used to produce a new image from the previous image with different pixel size, often called interpolation. Increasing the pixel size of the previous image is called upsampling and in this study we will only use twice the image magnification. Result/Findings: The results of each test method using advanced LSB without image scaling and advanced LSB using image scaling were compared to obtain detailed comparison results of each method. Novelty/Originality/Value: Advanced LSB and image scaling in this study can increase the message capacity three times compared to only using the advanced LSB method without image scaling. It depends on the image pixels used.

A new near-lossless image information hiding algorithm with controlled hiding capacity

Image information hiding technology can embed more data without increasing the amount of transmitted data. But in most cases, the information embedding rate is different for different carrier images. To increase the embedding rate, the data will be damaged. In order to improve this problem, a new near-lossless image information hiding algorithm with controlled hiding capacity is proposed, which is referred to as NLH. This method makes the pre-processed data in a specific range to facilitate information hiding by interval transformation and data mapping. According to analysis and calculation, the lossless performance of the proposed algorithm in this paper is better than the LSB algorithm. And the result of simulation shows that this method has a fixed information embedding rate (18.75%/1.5bpp) without more transmitted data, and the original image is restored losslessly or nearly losslessly with the improved transmission efficiency.

Android based Digital Steganography Application using LSB and PSNR Algorithm in Mobile Environment

Android based Digital Steganography Application using LSB and PSNR Algorithm in Mobile Environment

Improving the Security of Steganography in Video Using Genetic Algorithm

There has been a great development in information technology and methods of sending it via the Internet, but these developments expose the information transferred to violations of its security. To maintain information security, steganography and encryption are used. Hiding technology in multimedia, such as audio, video, text or pictures, provides an ability to protect information. Digital video is one of the most common data embedding media for its high ability to hide sensitive data. Where many methods have been proposed to hide the data in the video to prevent it from being seen by unauthorized persons. This paper proposes a new approach to hiding data in video by using two bitand and bitor processes without using the well-known LSB algorithm in the hiding method. In addition, confidential information is first encrypted using a genetic algorithm by which the encryption key is generated, which increases the strength of the proposed work. The experimental results showed that the used method is effective and safe as it provides a PSNR of 63, which makes the embedding capacity high.

Robust large image steganography using LSB algorithm and 5D hyper-chaotic system

This paper contains a robust hiding system proposed to hide and reconstruct efficiently a large size secret image based on merging encrypting and hiding. This approach deals with a video utilized as a cover of frames to hide the blocks of large image inside it. Frames are selected according to 5D hyperchaotic algorithms and consequently change value of the pixel in the original image depends on it. Secret image will divide into many blocks and hide each block in one frame selected by 5D chaotic. This method will support and enhance the techniques of information security with less time and morecapacity of transmission. The results obtained are analyzed under criteria including: Peak signal to noise ratio, Mean square error, key space and sensitivity, correlation coefficient and capacity of hiding. The results demonstrate both of the secret color image and cover video, where retaining its explicitness and properties after reconstruction in the receiver. However, the results prove stability and reliability of the proposed system under several conditions and can avoid attacks.

Secure image hiding in speech signal by steganography-mining and encryption

<span>Information hiding techniques are constantly evolving due to the increased need for security and confidentiality. This paper proposes a working mechanism in three phases. The first phase includes scrambling the values of the gray image depending on a series of keys that are generated using a quantum chaotic map. The second phase generates hybrid keys by mixing a Zaslavsky and a 3D Hanon map that are used to encrypt the gray image values produced after the scramble. Finally, in the third phase, a new algorithm is suggested to hide the encrypted gray image at random locations within a speech file. This algorithm includes the LSB algorithm to determine the hidden bits and the zero-crossing K-means algorithm in selecting locations mining in a scattered manner so that hackers cannot easily retrieve the hidden data of any hacked person. Also used a fractional fourier transform to choose magnitude value as specific data to hide encoded image data. The measures MSE, PSNR, NSCR, and UACI are using to measure the work efficiency in the encryption algorithm, and in measuring the efficiency of the hidden algorithm, use the measures SNR, PSNR, and MSE. The results of the paper are encouraging and efficient compared to other algorithms that performed the same work. Hence our results show the larger the image dimensions used, the better the values.</span>