scholarly journals CLSM: COUPLE LAYERED SECURITY MODEL A HIGH-CAPACITY DATA HIDING SCHEME USING WITH STEGANOGRAPHY

2017 ◽  
Vol 36 (1) ◽  
pp. 15 ◽  
Author(s):  
Cemal Kocak
Keyword(s):  
Signal To Noise Ratio ◽  
High Capacity ◽  
Irreducible Polynomial ◽  
Similarity Index ◽  
Quality Measurement ◽  
Structural Similarity ◽  
Hybrid Structure ◽  
Security Model ◽  
Pixel Value Differencing ◽  
Capacity Data

Cryptography and steganography are the two significant techniques used in secrecy of communications and in safe message transfer. In this study CLSM – Couple Layered Security Model is suggested which has a hybrid structure enhancing information security using features of cryptography and steganography. In CLSM system; the information which has been initially cryptographically encrypted is steganographically embedded in an image at the next step. The information is encrypted by means of a Text Keyword consisting of maximum 16 digits determined by the user in cryptography method. Similarly, the encrypted information is processed, during the embedding stage, using a 16 digit pin (I-PIN) which is determined again by the user. The carrier images utilized in the study have been determined as 24 bit/pixel colour. Utilization of images in .jpeg, .tiff, .pnp format has also been provided. The performance of the CLSM method has been evaluated according to the objective quality measurement criteria of PSNR-dB (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). In the study, 12 different sized information between 1000 and 609,129 bits were embedded into images. Between 34.14 and 65.8 dB PSNR values and between 0.989 and 0.999 SSIM values were obtained. CLSM showed better results compared to Pixel Value Differencing (PVD) method, Simulated Annealing (SA) Algorithm and Mix column transform based on irreducible polynomial mathematics methods.

QUALITY MEASURE OF THE COMPRESSED ECHO, X-RAY AND CT IMAGES

2013 ◽  
Vol 13 (01) ◽  
pp. 1350006 ◽  
Author(s):  
RAJANI GUPTA ◽  
PRASHANT BANSOD ◽  
R. S. GAMAD
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Quality Measurement ◽  
Structural Similarity ◽  
Set Partitioning ◽  
Underlying Structure ◽  
Biomedical Images ◽  
X Ray ◽  
Index Measurement

The paper reveals the analysis of the compression quality of true color medical images of echocardiogram (ECHO), X-radiation (X-ray) and computed tomography (CT) and further a comparison of compressed biomedical images of various sizes using two lossy compression techniques, set partitioning in hierarchical trees (SPIHT) and discrete cosine transform (DCT) to the original image is carried out. The study also evaluates the results after analyzing various objective parameters associated with the image. The objective of this analysis is to exhibits the effect of compression ratio on absolute average difference (AAD), cross correlation (CC), image fidelity (IF), mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity index measurement (SSIM) of the compressed image by SPIHT and DCT compression techniques. The results signify that the quality of the compressed image depends on resolution of the underlying structure where CT is found to be better than other image modalities. The X-ray compression results are equivalent by both the techniques. The compression results for large size biomedical images by SPIHT signifies that ECHO having comparable results to CT and X-ray while their DCT results are substandard. The compression results for comparatively smaller images of ECHO are not as good as X-ray and CT by both the compression techniques. The quality measurement of the compressed image has been designed using MATLAB.

MULTI-CHANNEL ECG-BASED STEGANOGRAPHY

2018 ◽  
Vol 30 (06) ◽  
pp. 1850046
Author(s):  
R. Boostani ◽  
M. Sabeti
Keyword(s):  
High Frequency ◽  
Average Length ◽  
Signal To Noise Ratio ◽  
High Capacity ◽  
Similarity Index ◽  
Structural Similarity ◽  
Primary Diagnosis ◽  
Nonlinear Feedback ◽  
Ecg Signals ◽  
Secure Information

There is a growing tendency for the concealment of secure information into electrocardiogram (ECG) signals in a way that the embedded ECGs still remain diagnosable. The average length of ECG recording for a primary diagnosis takes no longer than 1[Formula: see text]min yielding to limit its concealment capacity. To overcome this drawback, we enhanced both concealment capacity and embedding quality by: (I) using 12-lead ECGs to span more embedding space, (II) shuffling input message bits via nonlinear feedback shift register (NLFSR) method, (III) inserting the selected bits of each channel into the high-frequency wavelet coefficients of non-QRS parts. Inserting the message bits into high frequency coefficients of less important ECG parts leads to preserve the quality watermarked ECGs. To assess the proposed method, a text containing different letters (changes with size of both non-QRS segments and high-frequency sub-band) was hidden through 12-lead ECG signals of 56 randomly selected subjects of PTB database, where each signal length is 10[Formula: see text]s. The performance was compared to state-of-the-art ECG-based steganography schemes in terms of the following criteria: percentage residual difference (PRD), peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and bit error rate (BER). Our results showed that the proposed scheme has benefits of fast computing along with secure embedding, providing high capacity of data hiding.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Joint Encryption and Compression-Based Watermarking Technique for Security of Digital Documents

2021 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
A. K. Singh ◽  
S. Thakur ◽  
Alireza Jolfaei ◽  
Gautam Srivastava ◽  
MD. Elhoseny ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Digital Data ◽  
The Internet ◽  
Discrete Wavelet ◽  
Compression Performance ◽  
Strong Compression ◽  
Multiple Watermarks ◽  
Value Decomposition

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

2019 ◽  
Vol 17 (05) ◽  
pp. 1950035 ◽  
Author(s):  
Shenghan Mei ◽  
Xiaochun Liu ◽  
Shuli Mei
Keyword(s):  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Wavelet Denoising ◽  
Experimental Results ◽  
Transform Method ◽  
Denoising Method ◽  
Multi Scale ◽  
Piecewise Smoothness

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.

scholarly journals A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 946 ◽  
Author(s):  
Wenzhao Feng ◽  
Chunhe Hu ◽  
Yuan Wang ◽  
Junguo Zhang ◽  
Hao Yan
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Transmission Efficiency ◽  
Signal Interference ◽  
Progressive Transmission ◽  
Wireless Multimedia ◽  
Transmission Method ◽  
Hierarchical Coding ◽  
Object Region

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

scholarly journals Pothole Classification Model Using Edge Detection in Road Image

2020 ◽  
Vol 10 (19) ◽  
pp. 6662
Author(s):  
Ji-Won Baek ◽  
Kyungyong Chung
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Data ◽  
Structural Similarity ◽  
Detection Algorithm ◽  
Classification Model ◽  
Specific Object ◽  
Pixel Value ◽  
Distortion Rate

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

scholarly journals A NEW AND ADAPTIVE SECURITY MODEL FOR PUBLIC COMMUNICATION BASED ON CORRELATION OF DATA FRAMES

2019 ◽  
Vol 59 (2) ◽  
pp. 126-133
Author(s):  
Haider Tarish Haider ◽  
Dhiaa Halboot Muhsen ◽  
Haider Ismael Shahadib ◽  
Ong Hang See
Keyword(s):  
Information Technologies ◽  
High Capacity ◽  
Data Communication ◽  
Superior Performance ◽  
Secret Message ◽  
Communication Model ◽  
Security Model ◽  
Secret Key ◽  
Proposed Model ◽  
Capacity Data

Recent developments in communication and information technologies, plus the emerging of the Internet of Things (IoT) and machine to machine (M2M) principles, create the need to protect data from multiple types of attacks. In this paper, a secure and high capacity data communication model is proposed to protect the transmitted data based on identical frames between a secret and cover data. In this model, the cover data does not convey any embedded data (as in normal steganography system) or modify the secret message (as in traditional cryptography techniques). Alternatively, the proposed model sends the positions of the cover frames that are identical with the secret frames to the receiver side in order to recover the secret message. One of the significant advantages of the proposed model is the size of the secret key message which is considerably larger than the cover size, it may be even hundred times larger. Accordingly, the experimental results demonstrate a superior performance in terms of the capacity rate as compared to the traditional steganography techniques. Moreover, it has an advantage in terms of the required bandwidth to send the data or the required memory for saving when compared to the steganography methods, which need a bandwidth or memory up to 3-5 times of the original secret message. Where the length of the secret key (positions of the identical frames) that should be sent to the receiver increases by only 25% from the original secret message. This model is suitable for applications with a high level of security, high capacity rate and less bandwidth of communication or low storage devices.

A compensating total variation image denoising model combining L1 and L2 norm

2020 ◽  
pp. 002072092092330
Author(s):  
Liqiong Zhang ◽  
Min Li ◽  
Xiaohua Qiu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Isotropic Diffusion ◽  
Model Combining ◽  
L2 Norm ◽  
Staircase Effect

To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

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