2,927 research outputs found
An Image Hiding Scheme Based on Multi-bit-reference Substitution Table Using Dynamic Programming Strategy
[[abstract]]Simple least-significant-bit (LSB) substitution is the most straightforward way to embed the secret image in the host image. Based on the simple LSB substitution, the method using substitution table is proposed to improve the quality of the stego-image. In this paper, we shall bring up a new method that uses the un-embedded host pixel bits to partition the host pixel into different planes. This way, we can derive the optimal substitution table for each plane. By combining the optimal substitution tables, we can obtain the final result that we call the multi-bit-reference substitution table. After transforming the secret data according multi-bit-reference substitution table, we can embed the transformed secret data in the host image so that the host image will be degraded possibly less. The experimental results show that our method leads to good results
Multi-Stage Protection Using Pixel Selection Technique for Enhancing Steganography
Steganography and data security are extremely important for all organizations. This research introduces a novel stenographic method called multi-stage protection using the pixel selection technique (MPPST). MPPST is developed based on the features of the pixel and analysis technique to extract the pixel's characteristics and distribution of cover-image. A pixel selection technique is proposed for hiding secret messages using the feature selection method. The secret file is distributed and embedded randomly into the stego-image to make the process of the steganalysis complicated. The attackers not only need to deter which pixel values have been selected to carry the secret file, they also must rearrange the correct sequence of pixels. MPPST generates a complex key that indicates where the encrypted elements of the binary sequence of a secret file are. The analysis stage undergoes four stages, which are the calculation of the peak signal-to-noise ratio, mean squared error, histogram analysis, and relative entropy. These four stages are used to demonstrate the characteristics of the cover image. To evaluate the proposed method, MPPST is compared to the standard technique of Least Significant Bit (LSB) and other algorithms from the literature. The experimental results show that MPPST outperforms other algorithms for all instances and achieves a significant security enhancement
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Secure digital documents using Steganography and QR Code
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonWith the increasing use of the Internet several problems have arisen regarding the processing of electronic documents. These include content filtering, content retrieval/search. Moreover, document security has taken a centre stage including copyright protection, broadcast monitoring etc. There is an acute need of an effective tool which can find the identity, location and the time when the document was created so that it can be determined whether or not the contents of the document were tampered with after creation. Owing the sensitivity of the large amounts of data which is processed on a daily basis, verifying the authenticity and integrity of a document is more important now than it ever was. Unsurprisingly document authenticity verification has become the centre of attention in the world of research. Consequently, this research is concerned with creating a tool which deals with the above problem. This research proposes the use of a Quick Response Code as a message carrier for Text Key-print. The Text Key-print is a novel method which employs the basic element of the language (i.e. Characters of the alphabet) in order to achieve authenticity of electronic documents through the transformation of its physical structure into a logical structured relationship. The resultant dimensional matrix is then converted into a binary stream and encapsulated with a serial number or URL inside a Quick response Code (QR code) to form a digital fingerprint mark. For hiding a QR code, two image steganography techniques were developed based upon the spatial and the transform domains. In the spatial domain, three methods were proposed and implemented based on the least significant bit insertion technique and the use of pseudorandom number generator to scatter the message into a set of arbitrary pixels. These methods utilise the three colour channels in the images based on the RGB model based in order to embed one, two or three bits per the eight bit channel which results in three different hiding capacities. The second technique is an adaptive approach in transforming domain where a threshold value is calculated under a predefined location for embedding in order to identify the embedding strength of the embedding technique. The quality of the generated stego images was evaluated using both objective (PSNR) and Subjective (DSCQS) methods to ensure the reliability of our proposed methods. The experimental results revealed that PSNR is not a strong indicator of the perceived stego image quality, but not a bad interpreter also of the actual quality of stego images. Since the visual difference between the cover and the stego image must be absolutely imperceptible to the human visual system, it was logically convenient to ask human observers with different qualifications and experience in the field of image processing to evaluate the perceived quality of the cover and the stego image. Thus, the subjective responses were analysed using statistical measurements to describe the distribution of the scores given by the assessors. Thus, the proposed scheme presents an alternative approach to protect digital documents rather than the traditional techniques of digital signature and watermarking
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
Optimization of medical image steganography using n-decomposition genetic algorithm
Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications
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