Significant Bit Research Articles

Robust multifunctional watermarking system based zero-watermarking and polar harmonic Fourier moments

The pseudo-randomness exhibited by chaotic systems has proven valuable not only in plaintext information encryption of images with varying dimensions but also as a key signal for protecting image copyright information. Image copyright protection is a crucial part of image information protection. Currently, digital watermarking technology is predominantly employed for copyright protection of digital images. However, most of the existing watermarking technologies primarily focus on the improvement of watermarking performance, neglecting the security of the watermarking system itself. Drawing inspiration from the Latin square rule, this paper proposes a novel hyperchaotic mapping modulation framework, which is employed to construct a new 3D hyperchaotic mapping and enhance the security of the fragile watermark and the zero-watermark generation process. Additionally, it is integrated into the classical image encryption framework of scrambling-diffusion to safeguard meaningful copyright information. Moreover, a functional watermarking system is designed based on the RSA asymmetric encryption algorithm, least significant bit (LSB), and polar harmonic Fourier moments (PHFMs). The proposed system enables identity verification, tampering location, and copyright authentication simultaneously. During the generation of zero watermarks, the binarization threshold of the PHFMs information matrix is determined by the method of maximum variance. The experimental results demonstrate that this scheme achieves a high level of security, better robustness to both conventional and geometric attacks, and precise localization for different types of tampering attacks.

Vertical Layering of Quantized Neural Networks for Heterogeneous Inference.

Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained, transmitted, and stored for one specific hardware setting, incurring considerable costs in model training and maintenance. In this paper, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. It represents weights as a group of bits (vertical layers) organized from the most significant bit (also called the basic layer) to less significant bits (enhance layers). Hence, a neural network with an arbitrary quantization precision can be obtained by adding corresponding enhance layers to the basic layer. However, we empirically find that models obtained with existing quantization methods suffer severe performance degradation if adapted to vertical-layered weight representation. To this end, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism with the multi-objective optimization employed to train the shared source model weights such that they can be updated simultaneously, considering the performance of all networks. After the model is trained, to construct a vertical-layered network, the lowest bit-width quantized weights become the basic layer, and every bit dropped along the downsampling process act as an enhance layer. Our design is extensively evaluated on CIFAR-100 and ImageNet datasets. Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width.

A blind recovery technique with integer wavelet transforms in image watermarking

The development of internet technology has simplified the sharing and modification of digital image information. The aim of this study is to propose a new blind recovery technique based on integer wavelets transform (BRIWT) by utilizing their image content. The LSB adjustment technique on the integer wavelet transform is used to embed recovery data into the two least significant bits (LSB) of the image content. Authentication bits are embedded into the current locations of the LSB of the image content, while the recovery information is embedded into different block locations based on the proposed block mapping. The embedded recovery data is securely placed at random locations within the two LSBs using a secret key. A three-layer embedding of authentication bits is used to validate the integrity of the image contents, achieving high precision and accuracy. Tamper localization accuracy is employed to identify recovery bits from the image content. This research also investigates the image inpainting method to enhance recovery from tampered images. The proposed image inpainting is performed by identifying non-tampered pixels in the surrounding tamper localization. The results demonstrate that the proposed scheme can produce highly watermarked images with imperceptibility, with an average SSIM value of 0.9978 and a PSNR value of 46.20 dB. The proposed scheme significantly improves the accuracy of tamper localization, with a precision of 0.9943 and an accuracy of 0.9971. The proposed recovery technique using integer wavelet transforms achieves high-quality blind recovery with an SSIM value of 0.9934 under a tampering rate of 10%. The findings of this study reveal that the proposed scheme improves the quality of blind recovery by 14.2 % under a tampering rate of 80 %.

A comparative study of steganography using watermarking and modifications pixels versus least significant bit

This article presents a steganography proposal based on embedding data expressed in base 10 by directly replacing the pixel values from images red, green blue (RGB) with a novel compression technique based on watermarks. The method considers a manipulation of the object to be embedded through a data compression triple process via LZ77 and base 64, watermark from low-quality images, embedded via discrete wavelet transformation-singular value decomposition (DWT-SVD), message embedded by watermark is recovered with data loss calculated, the watermark image and lost data is compressed again using LZ77 and base 64 to generate the final message. The final message is embedded in portable network graphic (PNG) images taken from the Microsoft common objects in context (COCO), ImageNet and uncompressed color image database (UCID) datasets, through a filtering process pixel of the images, where the selected pixels expressed in base 10, and the final message data is embedded by replacing units’ position of each pixel. In experimentation results an average of 40 dB in peak signal noise to ratio (PSNR) and 0.98 in the similarity structural index metric (SSIM) evaluation were obtained, and evasion steganalysis rates of up to 93% for stego-images, the data embedded average is 3.2 bpp.

An image steganography using improved hyper-chaotic Henon map and fractal Tromino

<span lang="EN-US">Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.</span>

The Influence of Steganographic Methods and QR Code Resolutions on Data Hiding in Cover Images

Abstract This work deals with digital image processing using the steganography technique in real life. Various steganography techniques face data authentication, data security, and copyright issues. Due to the fact that the problem of data security is currently very topical, these techniques are often sought after. This is related to the constant development of new algorithms that provide solutions that will safely hide the secret message in the image and at the same time will be resistant to various types of attacks. The motivation for writing this work lies in the technique of representing a secret message using a Quick Response code. Later in this work is comparison between our results and results of approach made by scientific community, which works with data hiding within steganographic principles. The secret message is written in the least significant bit plane of the cover image, which ensures that the modification of the image cannot be detected by the naked eye. The proposed solution is evaluated by mathematical quality assessment parameters Peak signal-to-noise ratio, Mean squared error, Normalized cross-correlation and Structural similarity index.

A Bridged-Switch Energy-Efficient Switching Scheme for Successive Approximation Register Analog-to-Digital Converters with a Low-Complexity Capacitor Drive Circuit

In recent years, due to the rise of the Internet of Things (IoT), various sensors have come to be in great demand for IoT devices. Analog-to-digital converters (ADCs) act as an important part of receivers in sensors. To improve the uptime of IoT devices, a bridged-switch energy-efficient switching scheme for successive approximation register (SAR) ADCs with a low-complexity capacitor drive circuit is proposed. The technique of top-plate sampling and closed-loop charge recycling is used in the proposed switching scheme so that neither the first nor the second comparison consumes switching energy. The third comparison uses bridge switches to connect the subarray to the main array, effectively reducing switching’s energy consumption. Only the least significant bit (LSB) is dependent on the accuracy of Vcm; thus, the last comparison consumes little switching energy. The proposed switching scheme achieves an average switching energy value of 47.5 CV2ref, which is 96.52% lower than that of the conventional capacitor switching scheme and reduces the area by 75%. The other major circuit modules employed are bootstrapped switches, a fully dynamic comparator, and dynamic SAR logic. The proposed ADC was simulated under the conditions of 180 nm CMOS process and 1 MS/s, resulting in a 9.8-bit effective number of bits (ENOB), a signal-to-noise and distortion ratio (SNDR) of 60.76 dB, a spurious-free dynamic range (SFDR) of 69.85 dB, a power consumption of 14.7 μW, and a figure of merit (FoM) of 16.55 fJ/conv.-step.

Jaccard-based Random Distribution with Least and Most Significant Bit Hiding Methods for Highly Patients MRI Protected Privacy

In this study, the main goal is to improve patient care by making it easier for patient data and pictures to be sent between medical centers without problems. Still, one of the biggest problems with telemedicine is keeping patient information private and ensuring data is safe. This is especially important because even small changes to patient information could have serious consequences, such as wrong evaluations and lower-quality care. This study develops a new model that uses the unique Jaccard distribution of the least significant bit (LSB) and the most significant bit (MSB) to solve this complex problem. The goal of this model is to hide much information about a patient in the background of an MRI cover picture. The careful creation of this model is a crucial part of the current study, as it will ensure a solid way to hide information securely. A more advanced method is also suggested, which involves randomly putting private text in different places on the cover picture. This plan is meant to strengthen security steps and keep private patient information secret. The peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the mean square error (MSE) all improved significantly when this method was tested in the real world. With these convincing results, the study shows telemedicine is more effective than traditional methods for keeping patient data safe. This proves that the model and method shown have the potential to greatly improve patient privacy and data accuracy in telemedicine systems, which would improve the general quality of health care.

Message Hiding Using the Least Significant Bit Method with Shifting Hill Cipher Security

Technological developments go hand in hand with advances in digital messaging. In protecting the confidentiality of the message, it is necessary to double secure the data. This security can be done with a combination of steganography and cryptographic techniques. Steganography algorithm which is a technique for hiding messages well, one of which is Least Significant Bit (LSB). The LSB algorithm is a simple method because it only converts the value of the last bit in a message with the inserted message bit, which is a convenience of the LSB algorithm, but it becomes vulnerable to message theft attacks if not combined with other algorithms for security. So it is necessary to increase security. This research developed a combination method of LSB algorithm for steganography technique with Hill Cipher algorithm for cryptographic technique, Hill Cipher was developed with shifting (shifting) 2 (two) characters. With the development of this method, hackers will find it difficult to crack messages, and is expected to improve the performance of the algorithm in affecting image quality and travel time in running the algorithm. The results of this study will be tested using several evaluation tools MSE, PSNR, BER, CER, AE, and Entropy. With the development of this method, hackers will find it difficult to decipher messages, and from the results of this experiment has been able to improve the performance of the algorithm in maintaining image quality and can shorten travel time in running the algorithm.

Enhanced payload volume in the least significant bits image steganography using hash function

The art of message masking is called steganography. Steganography keeps communication from being seen by any other person. In the domain of information concealment within images, numerous steganographic techniques exist. Digital photos stand out as prime candidates due to their widespread availability. This study seeks to develop a secure, high-capacity communication system that ensures private interaction while safeguarding information from the broader context. This study used the four least significant bits for steganography to hide the message in a secure way using a hash function. Before steganography, the message is encrypted using one of the encryption techniques: Caesar cipher or Vigenère cipher. By altering only the least significant bits (LSBs), the changes between the original and stego images remain invisible to the human eye. The proposed method excels in secret data capacity, featuring a high peak signal-to-noise ratio (PSNR) and low mean square error (MSE). This approach offers significant payload capacity and dual-layer security (encryption and steganography).

Image Coding based Orthogonal Polynomials Multiresolution Analysis with Joint Probability Context Modeling and Modified Golomb-Rice Entropy Coding

The work proposes, a JPEG2000 like compression technique which is based on multiresolution analysis of orthogonal polynomials transformation (OPT) coefficients has been presented with bit modeling for Golomb-Rice entropy coding. Initially, the image under analysis is divided into blocks and OPT is applied to each divided blocks. Then, transformed coefficients are represented as sub bands like structure (multiresolution) and scalar quantization is carried out to the transformed coefficients to reduce the precision. The quantized coefficients are then bit modelled in the bit plane using a joint probability statistical model, and significant bits in the bit plane are chosen. On the selected relevant bits, a geometrically distributed set of context is modelled for further encoding with modified Golomb-Rice encoding to provide compressed data. The decompression procedure is just the reverse of compression procedure. Experiments and analysis are carried out to demonstrate the efficiency of the proposed compression scheme in terms of compression ratio and Peak-Signal-to Noise Ratio (PSNR), and the results are encouraging

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.

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

An Android-based Image Steganography Approach to Data Communication Security using LSB and Password-based Encryption

The study's ultimate goal is to develop an Android app dedicated to Image Steganography, a technique for concealing sensitive information behind seemingly innocuous photographs. This information hiding technique is essential because of its many potential uses. Symmetric encryption, asymmetric encryption, and steganography are all brought together in this method. The initial picture is encrypted using a symmetric technique in this procedure. After that, the encrypted picture is quietly placed using a least significant bits Steganographic approach to conceal the secret key that was encrypted using an asymmetric algorithm. The steganography algorithm's simple but effective security mechanism is a major benefit. Integrating a secret message into a seemingly benign source makes it very difficult to detect the hidden message without prior knowledge of its existence and the appropriate decryption algorithm. The privacy of the concealed data is protected by this feature. The planned Android app would provide a user-friendly interface for using picture steganography methods, making it simple for anybody to choose commonplace photos and secret messages for obfuscation. Both the original picture and the concealed message will be kept secret thanks to the app's robust encryption methods. The hidden message will be seamlessly included into the regular picture using advanced steganographic methods like the least significant bits approach. The creation of this software is motivated by a desire to provide an easily accessible solution for people who want safe communication through hidden messages or encrypted photos. The robust encryption and steganographic technologies, paired with the straightforward interface, will enable users to effectively protect sensitive information. This work will strengthen the area of data security and highlight the need of sophisticated encryption and steganography in modern digital communication.

Quantum color image watermarking scheme based on quantum error correction coding * *This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202000501) and National Natural Science Foundation of China (No. 61772295).

Quantum image processing, which merges classical image processing techniques with quantum computing, provides exceptional storage capacity and unparalleled parallel computing power. In this study, we present a quantum color image watermarking scheme that employs quantum error correction codes to address issues such as pixel loss and image distortion during watermark embedding and extraction. By utilizing the least significant bit method to embed the color values of the watermark image into those of the carrier image, we improve the scheme’s robustness. We also address the error correction capabilities of channel coding for phase-flip errors and follow the majority principle, resulting in more accurate extraction of the watermark image’s color and enhancing the watermarking scheme’s reliability and integrity. Our experimental simulations demonstrate that the proposed watermarking scheme boasts high security, strong robustness, and excellent concealment.

A multilayer model to enhance data security in cloud computing

<span>Cloud computing has introduced substantial advancements to the field of information technology, offering businesses enhanced features, flexibility, reliability, scalability, and a wide range of services. However, it also presents security challenges like data theft and manipulation. To minimize the risks associated with these threats, a model that depends on encryption and steganography is proposed with the aim of applying a security model in a cloud environment. The proposed cloud security model utilizes a multi-layer model that includes a Rivest-Shamir-Adleman (RSA), advanced encryption standard (AES), identity-based encryption (IBE) methods, least significant bit (LSB) method, and the Brotli method. This comprehensive approach effectively safeguards data integrity, confidentiality, and privacy against potential intruders. In addition, it enhances the flexibility and efficiency of the cloud by enabling the secure storage and transmission of large amounts of data.</span>

Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit

Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit

Analysis an optical communications system by using Optisystem program to transfer data over various distances

The transmission of data over long distances is a big challenge in the telecommunications industry. One way to achieve this is to use different wavelengths of light.In this article a comparison of wavelengths has been made to analysis of long-distance data transmission using three different wavelengths: 1625 nm, 1550 nm, and 810 nm. The purpose is to determine the most appropriate wavelength for transmitting data over distances from 60 km to 140 km, with increments of 20 km for each test. The rating is based on the performance of each wavelength in terms of signal quality, data integrity, and overall transmission efficiency. To attain this, a comprehensive trial setup is employed. The investigational framework involves transferring data signals at every wavelengths over an optical fiber, and simulating altered transmission distances in the definite range. Key factors such as signal strength, Q. factor and bit error rate (BER) are wisely observed and analyzed. The obtained results show distinct features for each wavelengths with esteem to long-distance data transmission over the experimental usage in the simulation application for these wavelengths then examining the above factors.

Cryptanalysis of RSA with composed decryption exponent with few most significant bits of one of the primes

Cryptanalysis of RSA with composed decryption exponent with few most significant bits of one of the primes

Optimizing Data Security with Hybrid Scheme Based on LSB and DWT

One of the most popular techniques in image steganography is the Least Significant Bit (LSB) operation, which involves inserting secret data into the cover image's pixels' least significant bit. However, the amount of secret data that can be concealed in the cover image depends on the number of bits used for embedding. In this paper, a novel hybrid steganographic system based on Discrete Wavelet Transform (DWT) and Least Significant Bit (LSB) for image steganography is proposed. The proposed scheme aims to optimize data security by utilizing LSB and DWT techniques to embed secret data into an image robustly and efficiently. The present paper focuses on evaluating the algorithm performance by comparing the encrypted image quality using metrics such as PSNR, RMSE, and SSIM. The experimental results showed that the suggested method outperforms the existing LSB-based in terms of security performance. These results indicated that the proposed method consistently outperforms the conventional LSB-based method across all bit depths tested, with an overall improvement of 14.12% in PSNR, 41.87% in RMSE, and 4.02% in SSIM over the traditional LSB. The proposed method enhanced the PSNR values, reduced the RMSE values, and increased the SSIM values for all bit depths tested, indicating higher image fidelity, less distortion, and better preservation of structural similarity of the original image.