Securing matrix counting-based secret-sharing involving crypto steganography

Secret sharing is an approach for accessing sensitive information by several authorized participants. It shares the secret key in different forms among several authorized participants, where a certain number of which is required to retrieve the original secret. This secret sharing raised a new practicality challenge of human secrecy memorization as well as limited number of users allowed, which have been addressed within this work. This paper examines two particular secret sharing techniques known as the counting-based secret sharing and the matrix-based secret sharing, which enhances the former to accommodate a high number of participants. This work aimed to serve participants accommodating the share’s memorization challenge via image steganography as well as testing both schemes in the cases of original 1-bit and 2-bit methods. We compared practicality applying the secret sharing methods combined with two image steganography methods: the least significant bit and the discrete wavelet transform. Our findings included interesting remarks regarding both security and robustness. Our results demonstrated that despite the large number of shares generated, as to serve more participants using the matrix-based scheme, its performance was highly satisfactory. Our experiments illustrated high imperceptibility levels for the matrix-based scheme, which in turn provides higher security and robustness compared to the original method, which provided very few shares serving a limited number of participants.

Increasing Participants Using Counting-Based Secret Sharing via Involving Matrices and Practical Steganography

Video steganography is a method that processes secure communication. When we see the history of steganography, it was hidden in many ways such as tablets covered with wax, & written on the stomachs of rabbits. Here in this paper, considering the video steganography methods to perform secure steganography communication. Many methods have been proposed for video steganography but they're no more different types of formats, secured, quality, of the results. So here propose secure steganography methods i.e. Secure base LSB method, Neural Networks & Fuzzy logic, and check their using PSNR and MSE data of the methods. That data-set has collected is from video streams. And the result was seen with the more formats, more security, quality of outputs, & accuracy values of PSNR & MSE which is better than other proposed methods.

Data Encryption & Decryption Using Steganography

In the present innovation, for the trading of information, the internet is the most well-known and significant medium. With the progression of the web and data innovation, computerized media has become perhaps the most famous and notable data transfer tools. This advanced information incorporates text, pictures, sound, video etc moved over the public organization. The majority of these advanced media appear as pictures and are a significant part in different applications, for example, chat, talk, news, website, web-based business, email, and digital books. The content is still facing various challenges in which including the issues of protection of copyright, modification, authentication. Cryptography, steganography, embedding techniques is widely used to secure the digital data. In this present the hybrid model of LSB steganography and Advanced Encryption Standard (AES) cryptography techniques to enhanced the security of the digital image and text that is undeniably challenging to break by the unapproved person. The security level of the secret information is estimated in the term of MSE and PSNR for better hiding required the low MSE and high PSNR values. 

Enhanced Digital Image and Text Data Security Using Hybrid Model of LSB Steganography and AES Cryptography Technique

In this modern era, where technology is increasing at rapid pace and made all day new developments, security is of highest priority. To protect facts into the unrecognizable form diverse technique are used for records hiding like steganography and cryptography techniques have been advanced. This paper securing the image by way of encryption is completed by LSB bits, DWT and RSA algorithm. This paper additionally presents new strategies wherein cryptography and steganography are mixed to scramble the information and in addition to cover the insights in some other medium through image processing (IP). The encrypted picture can be hiding in some other image by way of the use of LSB bits, DWT strategies so that the secret’s message exists. RSA algorithm applied, receiver will use his/her private key because the secret data have been encrypted by recipient public key. Hide encrypted image in the cover image by DWT. Extract encrypted image from cover image and decrypt text by DWT. The proposed scheme is implemented in MATLAB platform the use of preferred cryptography and steganography set of regulations. Calculate PSNR and MSE. Also calculate the entropy of cover image and stego image. This method is secure for communication in the digital world with the digital data transmission.

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Hide image and text using lsb, dwt and rsa based on image steganography

Machine Learning Manish Bhargav, Satish Kumar Alaria, Dr. Manish Kumar Mukhija M. Tech. Scholar, Department of Computer Science & Engineering, AIET, Jaipur Assistant Professor, Department of Computer Science & Engineering, AIET, Jaipur Associate Professor, Department of Computer Science & Engineering, AIET, Jaipur ABSTRACT: Twitter has turned into a tiny source of dynamic data for blogging places. People post on a wide range of topics and constantly communicate their assumptions, discuss current concerns, and positively review what they use in their daily lives on Twitter wall. The main goal is to assess the emotions expressed in tweets using various machine learning algorithms that identify tweets as positive or negative. If the tweet contains both negative and positive elements, the most dominant component should be chosen as the final component. In tweets, emojis, usernames, and hashtags must be managed and translated into a standard structure. Bigrams and unigrams, for example, must be removed as well. In any case, just relying on a single model, which did not give high accuracy, is taken into account when selecting a model with high precision. To be honest, organizers for these items have begun to investigate these modest internet journals (blogs) in order to get a general sense of their item. They frequently monitor and reply to client comments on smaller websites. One issue is coming up with new ways to recognize and abbreviate a broad sentiment. Several persons, such as Facebook, Twitter, and Instagram, were brought into interpersonal connection stages as recently as last year. Most people use social media to convey their feelings, ideas, or assumptions about objects, places, or people. Strategies Twitter, a micro-blogging platform, is a massive repository of public opinion for a variety of people, offers, businesses, and products, among other things. The public analysis system evaluations are known as sentiment assessment. Combination of sentiment analysis on Twitter give valuable context to what's being said on Twitter. The wide availability of internet exams and social media postings the media provides critical criticism to organizations in order to improve expert options and steer their marketing tactics to leisure and user selections. As a result, social media plays a key role in influencing the public's perception of the services or products chosen. The numerous tactics utilized for product classification critiques are highlighted in this study (which may be in the form of tweets) Tweet complaints to see if mass behaviour is positive, negative, or neutral. Analysis of the Product Market. The information used here comes from our Twitter product reviews, which were used to categorize opinions as satisfying.

Manish Mukhija

Papers

Hide image and text using lsb, dwt and rsa based on image steganography

Implementation of Sentiment Analysis and classification of Tweets using Machine Learning

Design and analysis of new algorithm for plant leaf species identification using by SVM and SMO