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.

A complete guide to start and improve in machine learning (ML)

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art

Louis-François Bouchard 3.3k Jan 04, 2023
Machine-Learning with python (jupyter)

Machine-Learning with python (jupyter) 머신러닝 야학 작심 10일과 쥬피터 노트북 기반 데이터 사이언스 시작 들어가기전 https://nbviewer.org/ 페이지를 통해서 쥬피터 노트북 내용을 볼 수 있다. 위 페이지에서 현재 레포 기

HyeonWoo Jeong 1 Jan 23, 2022
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022
LinearRegression2 Tvads and CarSales

LinearRegression2_Tvads_and_CarSales This project infers the insight that how the TV ads for cars and car Sales are being linked with each other. It i

Ashish Kumar Yadav 1 Dec 29, 2021
Machine Learning approach for quantifying detector distortion fields

DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model

Joel Bernier 1 Nov 05, 2021
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.

7.4k Jan 04, 2023
AutoOED: Automated Optimal Experiment Design Platform

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an

Yunsheng Tian 107 Jan 03, 2023
Pragmatic AI Labs 421 Dec 31, 2022
Azure MLOps (v2) solution accelerators.

Azure MLOps (v2) solution accelerator Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as the starting poi

Microsoft Azure 233 Jan 01, 2023
This repository demonstrates the usage of hover to understand and supervise a machine learning task.

Hover Example Apps (works out-of-the-box on Binder) This repository demonstrates the usage of hover to understand and supervise a machine learning tas

Pavel 43 Dec 03, 2021
Create large-scale ML-driven multiscale simulation ensembles to study the interactions

MuMMI RAS v0.1 Released: Nov 16, 2021 MuMMI RAS is the application component of the MuMMI framework developed to create large-scale ML-driven multisca

4 Feb 16, 2022
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
A concept I came up which ditches the idea of "layers" in a neural network.

Dynet A concept I came up which ditches the idea of "layers" in a neural network. Install Copy Dynet.py to your project. Run the example Install matpl

Anik Patel 4 Dec 05, 2021
Tools for mathematical optimization region

Tools for mathematical optimization region

林景 15 Nov 30, 2022
The Ultimate FREE Machine Learning Study Plan

The Ultimate FREE Machine Learning Study Plan

Patrick Loeber (Python Engineer) 2.5k Jan 05, 2023
CVXPY is a Python-embedded modeling language for convex optimization problems.

CVXPY The CVXPY documentation is at cvxpy.org. We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussio

4.3k Jan 08, 2023
The MLOps is the process of continuous integration and continuous delivery of Machine Learning artifacts as a software product, keeping it inside a loop of Design, Model Development and Operations.

MLOps The MLOps is the process of continuous integration and continuous delivery of Machine Learning artifacts as a software product, keeping it insid

Maykon Schots 25 Nov 27, 2022
BioPy is a collection (in-progress) of biologically-inspired algorithms written in Python

BioPy is a collection (in-progress) of biologically-inspired algorithms written in Python. Some of the algorithms included are mor

Jared M. Smith 40 Aug 26, 2022