Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Overview

Project Name : Steganography-Tools

Made By - Priyansh Sharma

  • Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.
  • This project hides the message with in the image, text file, audio file and video file. In this project, the sender selects a cover file (image, text, audio or video) with secret text and hide it into the cover file by using different efficient algorithm and generate a stego file of same format as our cover file (image, text, audio or video). Then the stego file is sent to the destination with the help of private or public communication networks. On the other side i.e. receiver, the receiver downloads the stego file and by using the appropriate decoding algorithm retrieves the secret text that is hidden in the stego file.

1

Image Steganography ( Hiding TEXT in IMAGE ) :

  • Using Least Significant Bit Insertion we overwrite the LSB bit of actual image with the bit of text message character. At the end of text message we push a delimiter to the message string as a checkpoint useful in decoding function. We encode data in order of Red, then Green and then Blue pixel for the entire message.

Text Steganography ( Hiding TEXT in TEXT ) :

  • In Unicode, there are specific zero-width characters (ZWC). We used four ZWCs for hiding the Secret Message through the Cover Text.

image

  • We get its ascii value and it is incremented or decremented based on if ascii value between 32 and 64 , it is incremented by 48(ascii value for 0) else it is decremented by 48
  • Then xor the the obtained value with 170(binary equivalent-10101010)
  • Convert the obtained number from first two step to its binary equivalent then add "0011" if it earlier belonged to ascii value between 32 and 64 else add "0110" making it 12 bit for each character.
  • With the final binary equivalent we also 111111111111 as delimiter to find the end of message
  • Now from 12 bit representing each character every 2 bit is replaced with equivalent ZWCs according to the table. Each character is hidden after a word in the cover text.

Audio Steganography ( Hiding TEXT in AUDIO ) :

  • For encoding we have modified the LSB Algorithm, for that we take each frame byte of the converting it to 8 bit format then check for the 4th LSB and see if it matches with the secret message bit. If yes change the 2nd LSB to 0 using logical AND operator between each frame byte and 253(11111101). Else we change the 2nd LSB to 1 using logical AND operation with 253 and then logical OR to change it to 1 and now add secret message bit in LSB for achieving that use logical AND operation between each frame byte of carrier audio and a binary number of 254 (11111110). Then logical OR operation between modified carrier byte and the next bit (0 or 1) from the secret message which resets the LSB of carrier byte.

Video Steganography ( Hiding TEXT in Video ) :

  • In video steganography we have used combination of cryptography and Steganography. We encode the message through two parts
  • We convert plaintext to cipher text for doing so we have used RC4 Encryption Algorithm. RC4 is a stream cipher and variable-length key algorithm. This algorithm encrypts one byte at a time. It has two major parts for encryption and decryption:-
  • KSA(Key-Scheduling Algorithm)- A list S of length 256 is made and the entries of S are set equal to the values from 0 to 255 in ascending order. We ask user for a key and convert it to its equivalent ascii code. S[] is a permutation of 0,1,2....255, now a variable j is assigned as j=(j+S[i]+key[i%key_length) mod 256 and swap S(i) with S(j) and accordingly we get new permutation for the whole keystream according to the key.
  • PRGA(Pseudo random generation Algorithm (Stream Generation)) - Now we take input length of plaintext and initiate loop to generate a keystream byte of equal length. For this we initiate i=0, j=0 now increment i by 1 and mod with 256. Now we add S[i] to j amd mod of it with 256 ,again swap the values. At last step take store keystreambytes which matches as S[(S[i]+S[j]) mod 256] to finally get key stream of length same as plaintext.
  • Now we xor the plaintext with keystream to get the final cipher.

With Further Development In this Project " Steganography Tools", This Project Can be used by Indian army, RAW, Police and Intelligence agency for Special Emergency operation.

AP1 Transcription Factor Binding Site Prediction

A machine learning project that predicted binding sites of AP1 transcription factor, using ChIP-Seq data and local DNA shape information.

1 Jan 21, 2022
A Time Series Library for Apache Spark

Flint: A Time Series Library for Apache Spark The ability to analyze time series data at scale is critical for the success of finance and IoT applicat

Two Sigma 970 Jan 04, 2023
Distributed deep learning on Hadoop and Spark clusters.

Note: we're lovingly marking this project as Archived since we're no longer supporting it. You are welcome to read the code and fork your own version

Yahoo 1.3k Dec 28, 2022
To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction. The challenge aims to adress the problems of medical imbalanced data classification.

Marwan Mashra 1 Jan 31, 2022
A Python toolkit for rule-based/unsupervised anomaly detection in time series

Anomaly Detection Toolkit (ADTK) Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. As

Arundo Analytics 888 Dec 30, 2022
MasTrade is a trading bot in baselines3,pytorch,gym

mastrade MasTrade is a trading bot in baselines3,pytorch,gym idea we have for example 1 btc and we buy a crypto with it with market option to trade in

Masoud Azizi 18 May 24, 2022
Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes Scikit-Learn useful pre-defined Pipelines Hub Usage: Install scikit-pipes It's advised to install sklearn-genetic using a virtual env, in

Rodrigo Arenas 1 Apr 26, 2022
Predicting job salaries from ads - a Kaggle competition

Predicting job salaries from ads - a Kaggle competition

Zygmunt ZajÄ…c 57 Oct 23, 2020
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end

Najibulloh Asror 3 Feb 10, 2022
Made in collaboration with Chris George for Art + ML Spring 2019.

Deepdream Eyes Made in collaboration with Chris George for Art + ML Spring 2019.

Francisco Cabrera 1 Jan 12, 2022
MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data

MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data. We demonstrate its use

Pachter Lab 26 Nov 29, 2022
Learn Machine Learning Algorithms by doing projects in Python and R Programming Language

Learn Machine Learning Algorithms by doing projects in Python and R Programming Language. This repo covers all aspect of Machine Learning Algorithms.

Ravi Chaubey 6 Oct 20, 2022
Practical Time-Series Analysis, published by Packt

Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj

Packt 325 Dec 23, 2022
a distributed deep learning platform

Apache SINGA Distributed deep learning system http://singa.apache.org Quick Start Installation Examples Issues JIRA tickets Code Analysis: Mailing Lis

The Apache Software Foundation 2.7k Jan 05, 2023
Meerkat provides fast and flexible data structures for working with complex machine learning datasets.

Meerkat makes it easier for ML practitioners to interact with high-dimensional, multi-modal data. It provides simple abstractions for data inspection, model evaluation and model training supported by

Robustness Gym 115 Dec 12, 2022
Decision Weights in Prospect Theory

Decision Weights in Prospect Theory It's clear that humans are irrational, but how irrational are they? After some research into behavourial economics

Cameron Davidson-Pilon 32 Nov 08, 2021
cleanlab is the data-centric ML ops package for machine learning with noisy labels.

cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and lear

Cleanlab 51 Nov 28, 2022
A single Python file with some tools for visualizing machine learning in the terminal.

Machine Learning Visualization Tools A single Python file with some tools for visualizing machine learning in the terminal. This demo is composed of t

Bram Wasti 35 Dec 29, 2022
High performance implementation of Extreme Learning Machines (fast randomized neural networks).

High Performance toolbox for Extreme Learning Machines. Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks, which sol

Anton Akusok 174 Dec 07, 2022
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Microsoft 14.5k Jan 07, 2023