Enhanced security of EMI using an edge-based steganography with four-layered cryptography

Focusing on enhancing the security and privacy of electronic medical images (EMI) which have varying properties and larger in number before they are stored or transmitted by hospitals, labs, and researchers or while they reside in third party storage. EMI should be anywhere, anytime accessible for referencing and medical diagnosis. Therefore, should be accessible in real-time through real-time applications (RTA). This research work uses a large data set having 5856 X-ray images of a total size of 1.6 GB in JPEG format each of these X-ray images vary in properties such as size and dimensions. Two methods have been implemented for enhancing security and privacy Edge-Based Steganography (EBS) and Block-Based Steganography (BBS) in the region of no interest (RONI) of the normalized cover image. Both methods are compared for enhancement concerning steganography properties such as payload capacity, imperceptibility, security, reduction in computational time, and lesser computational complexity which increases their applicability for RTA. RTA makes EMI accessible on heterogeneous devices, therefore, increasing its scalability and usage. This paper performs comparative evaluation based on standardized tests such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), Coefficient of Variation (CV), and Pearson Correlation (R), etc. Results show that both methods achieved a 100% embedding rate, SSIM value close to 1, and low average MSE value of 0.005 for EBS secret images and 0.003 for BBS secret images. Results indicate both methods are capable of hiding large data sets within minutes therefore making them both suitable for RTA while the EBS method takes 2.3 minutes which is less than BBS’s 3.6 minutes encoding time for 5856 images. Therefore, the EBS method was found more suitable than the BBS method based on computational time.

We propose a watermarking method that incor-porates a quantized activation function to provide robustness against quantization. Zhu et al. showed that the introduction of a noise layer between the encoder and decoder can increase the robustness against attacks. Although there are various attacks on stego-images, these images are often JPEG-compressed. As the process of JPEG compression includes quantization, the watermark decoder must be able to estimate watermarks from compressed images. Hence, we propose a quantization layer that introduces a quantized activation function consisting of the hy-perbolic tangent function. The proposed neural network is based on that proposed by Hamamoto and Kawamura. By simulating the quantization of JPEG compression, the quantization layer is expected to improve the robustness against JPEG compression. The robustness was evaluated by the bit error rate (BER), and the stego-image quality was evaluated by the peak signal-to-noise ratio (PSNR). The proposed network achieved a high image quality of more than 35 dB, and it could extract watermarks with a BER of less than 0.1 for Q-values of 30 or higher in JPEG compression. It was thus more robust against JPEG compression than Hamamoto and Kawamura's model.

In this paper, we propose DCT-domain robust data hiding algorithm using chaotic sequence. The algorithm works by dividing the cover into blocks of equal sizes and then embeds the watermark in middle band of DCT coefficient. Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using the bit error rate (BER) and the peak signal to noise ratio (PSNR) value for cover image and watermarked image. This algorithm is compared to PN sequence based DCT algorithm. The proposed algorithm provides more robustness against several common image processing attacks, such as JPEG compression, low-pass filtering and addition of noise. In case of JPEG compression attacks, for low quality compression (Q-60) it has been analyzed that more than 92% of the hidden data was recovered without any error and for same quality compression in PN sequence based DCT algorithm has more than 89% of the hidden data recovered without any error.

In this chapter, a successful attempt for transmission of secured compressed data over GSM voice channel has been made. In this work, given text of any length is converted to real-time speech signal and then compressed with discrete wavelet transform, encoded with gold code sequence, modulated using quadrature phase-shift keying and sent over the GSM voice channel. The aim of this paper was to present a secured digital data compression and modulation for robust data transmission in terms of voice. The main objective was to achieve higher data rates, lower bit error rate, and less utilization of the bandwidth. The performance of the proposed technique is compared to an already existing technique for data transmission over voice channel. It has been observed that the proposed technique shows much better results in terms of bit error rate (BER), mean square error (MSE), and peak signal-to-noise ratio (PSNR) as compared to the already existing techniques. The proposed scheme has a great significance in GSM systems where data security and quality of service (QoS) are two main issues.

Utilization of a dependency-tracking language to reduce computational time during multidisciplinary design optimization

This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.

Watermarking is crucial for protecting medical images, guaranteeing data integrity and authenticity in telemedicine, especially for rural healthcare facilities with limited computational power. This paper introduces a lightweight watermarking scheme that performs a comparative study of Lifting Wavelet Transform (LWT) versus Discrete Wavelet Transform (DWT), both combined with Discrete Cosine Transform (DCT), to achieve optimal imperceptibility, robustness, and computational efficiency. The comparison of LWT and DWT is essential because LWT has lower computational complexity and memory usage, hence more appropriate for resource-limited environments than DWT, and without the use of Singular Value Decomposition (SVD) to further reduce processing requirements and improve compatibility with low-power devices. The suggested approach adopts adaptive embedding against texture complexity, secure synchronization patterns with m-sequences and Barker codes, and geometric correction utilizing ORB for guaranteed watermark extraction under attacks from JPEG compression, Gaussian noise attack, rotation attack, median filter attack, and Gaussian blur. A new error correction code interleaved with majority voting is employed to increase the watermark’s strength. Tested at various embedding strengths, the scheme attains Peak Signal to Noise Ratio (PSNR) values over 40 dB and Structural Similarity Index (SSIM) values above 0.95, signifying extremely little visual degradation. Low Bit Error Rates (BER) and high Normalized Correlation (NC) in various attack scenarios validate its success. This computational-friendly solution is designed to arm remote healthcare organizations with limited computer ability to reliably authenticate and attest to medical images, meeting severe telemedicine demands.

An investigation of unequal error protection (UEP) methods applied to JPEG image transmission using turbo codes is presented. The JPEG image is partitioned into two groups, i.e., DC components and AC components according to their respective sensitivity to channel noise. The highly sensitive DC components are better protected with a lower coding rate, while the less sensitive AC components use a higher coding rate. Simulation results are given to demonstrate how the UEP schemes outperforms the equal error protection (EEP) scheme in terms of bit error rate (BER) and peak signal to noise ratio (PSNR).

Uncompressed video transmission has recently attracted research attentions since it ensures ultra-low latency, high video quality, and low complexity. In this paper, we propose a real-time uncompressed video transmission system, where unequal power allocation (UPA) strategies are adopted to maximize the peak signal-to-noise ratio (PSNR) while considering the peak-to-average power ratio (PAPR) reduction. Specifically, the relationship between the bit error rate (BER) and the mean square error (MSE) of this system is established. Based on the relationship, a group of optimization problems are formulated and UPA schemes are proposed through solving these problems. Among all strategies, UPA with an adaptive Hadamard switch is finally adopted in the proposed system, because it outperforms other schemes in PSNR performance under both the total power constraint and the PAPR constraint. Simulation results show that the PSNR of the reconstructed video is improved largely compared with reference methods.

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

The growth of modern wireless communications has increased the demand for high quality data services. This article presents an analysis of the performance of a DVB-S2 system with MIMO technology. A method for using a receiver-transmitter based on DVB-S2 with MIMO technology, provides a better quality of service for data transmission and discusses ways to improve the quality of data transmission using size and type converters of transmitting and receiving data, an amplifier and a normalizer Gain. The circuit was examined for bit error rate (BER) and peak signal-to-noise ratio (PSNR) between AWGN and multipath Rayleigh fading images operating under intersymbol interference (ISI). The analysis was carried out and a graph of the influence of ISI was obtained. The proposed system has the best quality of service for data transmission for fading channels. The transmission of images in the system includes a Rayleigh channel and AWGN with different values of the signal-to-noise ratio (SNR, dB) and different values of Gain. The Gain values change the BER and PSNR values (7.834 dB, 8.385 dB, and inf) at an SNR of -58 dB with respect to Gain values of 1, 32400 and 648000, respectively. Also obtained eye diagrams, where you can see a decrease in the negative impact of intersymbol interference with an increase in the value of Gain..This system can be used to wirelessly transmit higher quality, high-resolution radiological images (such as computed tomography or magnetic resonance imaging) over a wireless network to remote healthcare workers.

In the technological era, the primary source of information is in the form of digital data, which has to be secured while storing or transmitting during communication over an unsecured network. Different approaches are used to provide security to digital data, viz. text, audio, image, and video. This paper initially explains the security system such as cryptography, watermarking, and steganography and their comparative analysis based on different characteristics, viz. satisfaction level of objective, type of carrier object and secret information to be used, dependency of security level, and quality assessment parameters. This review article focuses more on steganography methods applied over video. The various methods implemented for video steganography in compressed domain, viz. inter-frame and intra-frame prediction, motion vector estimation, entropy coding (CAVLC and CABAC), and transformed and quantized coefficients of DCT, DST, and DWT, etc. and the methods based on spatial and transform domain for uncompressed video are briefly described. It is followed by the detailed analysis of related work done by various researchers in video steganography and the obtained experimental results. Furthermore, the confidential data hiding in compressed videos are explained using Moving Picture Expert Group (MPEG—1, MPEG—2, MPEG—4), Advanced Video Coding (AVC)/H.264, and High-Efficiency Video Coding (HEVC)/H.265 that includes both spatial and transform domain. This paper summarizes and explains the detailed investigations of numerous techniques of video steganography based on the comprehensive literature survey. The methods used to assess the performance of video steganography are analyzed based on the quality assessment parameters such as imperceptibility; measured by peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity (SSIM), robustness; measured by bit error rate (BER) and similarity (Sim), and embedding capacity; measured by hiding ratio. The overall review of past literature facilitates to have in-depth knowledge for upgrading the video steganography.

Background and Objective: The public and group communication carried out in the social, cloud, and mobile networks suffer from unauthorized users. The data security is required for secure authentication, monetary transactions and other sensitive information. The steganography methods empower the user with invulnerable communication in public domain. In this paper, a region secure and pattern based alternate 2B-3C (2Bit-3Channel) Steganography method is proposed to improve the content reliability and data security. Methods: In this method, the cover pixels are retrieved by applying the spiral rectangular pattern over the cover image. This spiral pattern also secures the core content region and the pixels from the core ROI (Region of Interest) is used as a cover only for larger size secret information. In this proposed steganography method, two channels are considered alternatively based on the defined rules. The LSB (Least Significant Bit) of both channels is XOR with input data bits and key to perform data hiding. The method is applied on real time color images taken from COREL and USC-SIPI-ID. The evaluation of proposed steganography approach is done using MSE (Mean Square Error), PSNR (Peak Signal-to-Noise Ratio), BER (Bit Error Rate), NCC (Normalized Cross Correlation) and SSIM (Structural Similarity Index Matrix). Result: The comparative results are generated for 50 real time images against the existing LSB (Least Significant Bit), LSBM (Least Significant Bit Modulation), LSBMR (Least Significant bit Matched Revisited), SCC (Stego Color Cycle), SHSI (Simple Hue-Saturation-Intensity), HIS-MLSB(Simple Hue-Saturation-Intensity-Modified LSB) and IMMEA(Iterative Magic Matrix Encryption Algorithm) methods. Conclusion: The experimentation results verified that the proposed 2B-3C approach improved the data hiding in qualitative and quantitative aspects.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

Due to light scattering by air particles, images taken under bad lighting conditions (such as haze, fog, mist, or smog) have a reduced level of visibility. One blurry image can be made visible again using single image dehazing techniques. Due to the ill-posedness of the Single image dehazing problem, it is difficult to solve. Even though, the Dark channel prior (DCP) has been the most prevalent method for image dehazing algorithms, but it suffers from huge computational time and picture quality. The accurate transmission map estimation is one of the best preferred ways to achieve the least computational time and high picture quality in DCP. Hence, this work is focused on constructing transmission map of a hazy image based on feed forward artificial neural network. The recommended method uses a feed forward ANN to conduct the transmission map straight from the minimum channel and a normalizing procedure to increase the re-covered image information. With a training set of eighty images, the network is trained by means of mean square error (MSE). The proposed method utilizing peak signal to noise ratio (PSNR) and structural similarity (SSIM) index measures are used to assess the restoration quality. The investigational conclusions have demonstrated that, the suggested method outperforms the dehazing of an input image without degrading visible quality (PSNR = 69.16, SSIM index = 0.8913). In addition, the proposed method is suited for real-time applications given the average computing time it achieves is 1.03 s.