3-Level security based spread spectrum image steganography with enhanced peak signal to noise ratio

2017 ◽  
Author(s):  
Poonam Yadav ◽  
Maitreyee Dutta
Keyword(s):  
Spread Spectrum ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

scholarly journals Rancang Bangun FM Transmitter Menggunakan Metode Spread Spectrum Signal Berbasis Raspberry Pi

2021 ◽  
Vol 11 (3) ◽  
pp. 141-144
Author(s):  
Achmad Hanif ◽  
Aad Hariyadi ◽  
Aisah Aisah
Keyword(s):  
Spread Spectrum ◽  
Signal To Noise Ratio ◽  
Raspberry Pi ◽  
Signal To Noise ◽  
Noise Ratio

Kebutuhan akan sistem Komunikasi yang dapat mengatasi masalah interferensi, dapat menjamin kerahasiaan informasi yang dikirim dan dapat beroperasi pada tingkat S/N  (Signal to Noise Ratio)  yang rendah atau tahan terhadap pada derau yang besar.Salah tekonologi dalam sistem komunikasi adalah sistem spread spectrum. Karakteristik penting dari komunikasi spread-spectrum adalah bahwa dapat memberikan perlindungan terhadap sinyal yang mengganggu dengan daya yang terbatas. SBC  (Raspberry pi) dapat diimplementasikan menjadi sebuah pemancar FM dengan menggunakan socket SOC Broadcom bcm 2837 yang akan menghasilkan sebuah base clock,  kemudian sinyal output akan dihasilkan melalui pin GPIO 7.

scholarly journals An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

2020 ◽  
Author(s):  
Siming He ◽  
Jian Guan ◽  
Xiu Ji ◽  
Hui Wang ◽  
Yi Wang
Keyword(s):  
Data Processing ◽  
Spread Spectrum ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Induced Polarization ◽  
High Resistivity ◽  
Observation System ◽  
Identification Algorithm ◽  
Signal To Noise ◽  
Noise Ratio

Abstract. In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The traditional correlation identification (TCI) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the formula of the TCI algorithm is used for identifying the frequency response of the observation system. Even when the signal to noise ratio (SNR) is −37.5 dB, this ECI algorithm can still be able to keep 3.0 % relative error. Experiments on both synthetic and real SSIP data show that the ECI algorithm can not only suppress the background noise but also better preserves the valid information of the raw SSIP data to display the actual location and shape of adjacent high resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging.

Multilevel Secure Digital Image Steganography Framework Using Random Function and Advanced Encryption Standard

2019 ◽  
Vol 16 (11) ◽  
pp. 4812-4825
Author(s):  
Mohsin N. Srayyih Almaliki
Keyword(s):  
Image Quality ◽  
Random Function ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Secret Message ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Preparation Phase ◽  
Noise Ratio

One of the crucial aspects of processes and methodologies in the information and communication technology era is the security of information. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies which are used, and they include steganography and cryptography. With cryptography, the secret message is converted into unintelligible text, but the existence of the secret message is noticed, nonetheless, steganography involves hiding the secret message in a way that its presence cannot be noticed. In this paper, a new secure image steganography framework which is known as an adaptive stego key LSB (ASK-LSB) framework is proposed. The construction of the proposed framework was carried out in four phases with the aim of improving the data-hiding algorithm in cover images by using capacity, image quality, and security. To achieve this, the Peak Signal-to-Noise Ratio (PSNR) of the steganography framework was maintained. The four phases began with the image preparation phase, followed by the secret message preparation phase, embedding phase and finally extraction phase. The secure image steganography framework that is proposed in this study is based on a new adaptive of least significant bit substitution method, combination random function, and encryption method. In the proposed work, the secret bits are inserted directly or inversely, thereby enhancing the imperceptibility and complexity of the process of embedding. Results from the experiment reveal that the algorithm has better image quality index, peak signal-to-noise ratio, and payload used in the evaluation of the stego image.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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