Empowering journalists and whistleblowers

Overview

Onymochat

Empowering journalists and whistleblowers

Version Python Versions Release Testted_On MIT License PR's Welcome

Onymochat is an end-to-end encrypted, decentralized, anonymous chat application. You can also host your anonymous .onion webpage with Onymochat.

  • Onymochat works over the Tor.
  • Anyone can start their own chat server from their own PC.
  • It's end-to-end encrypted.
  • It's basically magic.

Features

  • Start your own chat server for two or more users from your own PC.
  • Users can get connected to a chat server using the public key of the server.
  • You can launch your chat client and chat with anyone who has your public key and server details (after he/she/they joins the server).
  • You can launch your own anonymous .onion webpage with Onymochat. You can use this anonymous website for your journalistic works and whistleblowing.

You don't have to rely upon any third-party app to provide you with a platform/server to anonymously chat with your friend. You can host your own server and if the person you want to chat with has the server's public key, he/she/they can join the server with his/her/their chat client and chat with you.

Multiple people can use a single chat server. The chat data is deleted whenever the server is closed. The chat is end to end encrypted, so even if someone hacks into the server somehow, they won't be able to get to know what two people are talking about. It uses 4096 bit RSA keys for encryption. You connect to the chat server over the Tor network, which gives you anonymity.

Security

Let's see what makes Onyomochat a secure chat application:

  • End-to-end 4096 bit RSA encryption for messages.
  • Version 3 Onion Service for your .onion webpage.
  • Version 3 Onion Service for your chat server.
  • Connection to server over the Tor network.

Installation

Environment Setup

Onymochat requires Python 3.9 or above to run. I have tested it with Python 3.9. Make sure that you have Python added to your PATH. When you install Python in your Windows system, make sure to check 'Add Python 3.x to PATH'. If you forget to do it, see this tutorial to know how to add Python to your PATH for Windows.

Install Python

For Windows and Mac

Download Python 3.9 from here. Use the installer to install Python in your System. Download 'macOS 64-bit universal2 installer' for Mac OS. Download 'Windows x86-64 executable installer' for your Windows 64 Bit system and 'Windows x86 executable installer' for Windows 32 bit system.

For Linux

Use the following command to install Python 3.9 on your Linux system.

apt-get install python3.9

Check pip

Make sure you have pip installed in your system. Use the following command to check if you have pip installed.

pip --version

If you see a message like 'pip 21.2.2' then you have pip installed on your system. Otherwise, follow this tutorial to install pip in your system. Generally, Python comes with an ensurepip module, which can install pip in a Python environment.

python -m ensurepip --upgrade

Download Repository

Go to the GitHub repository of Onymochat: https://github.com/SamratDuttaOfficial/onymochat

Click on the green 'Code' button and click on 'Download ZIP' and unzip the archive somewhere to use Onymochat.

Or, use the command below if you have git installed in your system.

git clone https://github.com/SamratDuttaOfficial/onymochat

Install Requirements

Open up your terminal (CMD on Windows) and go to the folder where you've cloned/unzipped Onymochat. Example:

cd C:\User\Desktop\Onymochat-master

Then install all the requirements from the requirements.txt file.

Windows:

pip install -r requirements.txt

Linux and Mac OS:

pip3 install -r requirements.txt

If you're on Linux, you might need to install Tkinter separately in the following way:

sudo apt install python3-tk

This will install all of the requirements, except Tor.

Install Tor

Download and install Tor browser from the official Tor Project website: https://www.torproject.org/download/

Take a note of where you're installing Tor/Tor Browser, it will be required later.

How to Use

After installation, open the 'onymochat' subdirectory in your terminal. This directory should have a file like run_onymochat.py. Run this file.

python run_onymochat.py

If you are on Linux, run that file using the following command instead:

python3 run_onymochat.py

This will run the Onymochat program in your terminal. This will greet you with a menu. Just input the number of the option you want to go to, and hit the enter button.

First, configure Onymochat with Tor.

Configure Onymocaht with Tor

Run the program and go to option 0 (zero).

Then, on the next prompt, enter the path to tor.exe in the TorBrowser folder. This is important to configure Onymochat with Tor. You have to do this step only once after installation. Paste the path to tor.exe in the TorBrowser (or any similar name) folder.

Example (For Windows): C:\user\Desktop\Tor Browser\Browser\TorBrowser\Tor\tor.exe

Example (For Mac): Applications\TorBrowser.app\Tor\tor.real

Linux users just write 'tor' without the quotations.

Now you are ready to use Onymochat

Things You Can Do

Here are all the things you can do with Onymochat.

  1. Create new hidden service and chat server
  2. Generate encryption keys for chat
  3. Run chat client
  4. Create onion webpage
  5. Generate QR codes for your encryption keys
  6. Generate QR codes for other keys
  7. Delete all saved keys
  8. Exit

How to Chat?

Here are some steps you need to follow to chat with someone through Onymochat.

CAUTION: NEVER SHARE ANY OF YOUR PRIVATE KEYS WITH ANYONE

Step 1

First, select option 1 to create a new hidden service and server and follow the instructions given in your terminal/command window. This will be the server where the chat data will be temporarily saved (all chat data will be lost when the hidden service and server is closed). You can press Ctrl + C to close this hidden service and server when you are done chatting.

Then, share the hidden service public key with someone you want to chat with. You can do it in person by meeting that person, or through any other communication method. You can use the same hidden service (same public key) to chat with multiple persons but this comes with the risk of sharing the same keys with everyone, and someone might use them later to spam you. Or, the other person, with whom you want to chat with, can provide you with his/her/their hidden service public key and you can use it too.

Step 2

Select option 2 to generate encryption keys for your chat. You need to share your public key with any person you want to chat with.

Step 3

Select option 3 to run your chat client. There you won't need to create any new encryption keys for chatting if you don't want to. Creating more than one key will be very hard to manage and might be the reason of some problems in future.

You will need to enter your or the other person's hidden service and server's public key and also the other person's public key for encryption to chat with that person.

How to Create an Anonymous (.onion) Webpage

Step 1

In the 'onymochat' directory, go to the 'onion_webpage' directory. Edit the index.html HTML according to your preference. This will be the page for your anonymous webpage.

Step 2

Select option 4 from the main menu. You can generate a new URL for your .onion webpage and save the private key of that webpage to resume the webpage later with the same URL. Or, you can use a pre-saved private key to resume your website with a particular URL you've generated before.

Generate QR Codes for Encryption Keys

Option 5, and 6 is to generate QR codes for different keys used in Onymochat. These QR codes are saved in \files\qr_codes. You can print them and share them with other people you want to communicate with.

Delete all saved keys and QR codes

Use option 7 to delete all saved public and private keys and QR codes from your system. Use this option only when you suspect a security breach.

Exit Program

Exit the program by selecting option 8 from the main menu.


Author

Created by Samrat Dutta

Github: https://github.com/SamratDuttaOfficial

Linkedin: https://www.linkedin.com/in/SamratDuttaOfficial

Buy me a coffee: https://www.buymeacoffee.com/SamratDutta


Github

Please visit the Github repository to download Onymochat and see a quick tutorial.

https://github.com/SamratDuttaOfficial/onymochat

Pull requests are always welcome.

Owner
Samrat Dutta
Developer, designer, writer. Developer of CoWiseCare.
Samrat Dutta
PyTorch Implementation of "Light Field Image Super-Resolution with Transformers"

LFT PyTorch implementation of "Light Field Image Super-Resolution with Transformers", arXiv 2021. [pdf]. Contributions: We make the first attempt to a

Squidward 62 Nov 28, 2022
Gradient Step Denoiser for convergent Plug-and-Play

Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"

Samuel Hurault 11 Sep 17, 2022
Unsupervised Foreground Extraction via Deep Region Competition

Unsupervised Foreground Extraction via Deep Region Competition [Paper] [Code] The official code repository for NeurIPS 2021 paper "Unsupervised Foregr

28 Nov 06, 2022
Seeing if I can put together an interactive version of 3b1b's Manim in Streamlit

streamlit-manim Seeing if I can put together an interactive version of 3b1b's Manim in Streamlit Installation I had to install pango with sudo apt-get

Adrien Treuille 6 Aug 03, 2022
A simple image/video to Desmos graph converter run locally

Desmos Bezier Renderer A simple image/video to Desmos graph converter run locally Sample Result Setup Install dependencies apt update apt install git

Kevin JY Cui 339 Dec 23, 2022
Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro

Eric Mitchell 28 Jan 01, 2023
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
PyQt6 configuration in yaml format providing the most simple script.

PyamlQt(ぴゃむるきゅーと) PyQt6 configuration in yaml format providing the most simple script. Requirements yaml PyQt6, ( PyQt5 ) Installation pip install Pya

Ar-Ray 7 Aug 15, 2022
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

Shipeng Wang 34 Dec 21, 2022
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval - ICCV2021

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval Pytorch implementation of SPQ Accepted to ICCV 2021 - paper Young Kyun Jang

Young Kyun Jang 71 Dec 27, 2022
Bringing Computer Vision and Flutter together , to build an awesome app !!

Bringing Computer Vision and Flutter together , to build an awesome app !! Explore the Directories Flutter · Machine Learning Table of Contents About

Padmanabha Banerjee 14 Apr 07, 2022
The official implementation for ACL 2021 "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval".

Code for "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval" (ACL 2021, Long) This is the repository for baseline m

Akari Asai 25 Oct 30, 2022
This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition This is the offical website for paper ''

Wanglong Lu 28 Oct 29, 2022
Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

RHGN Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling Dependencies torch==1.6.0 torchvision==0.7.0 dgl==0.7.1

Big Data and Multi-modal Computing Group, CRIPAC 6 Nov 29, 2022
Learn other languages ​​using artificial intelligence with python.

The main idea of ​​the project is to facilitate the learning of other languages. We created a simple AI that will interact with you. Just ask questions that if she knows, she will answer.

Pedro Rodrigues 2 Jun 07, 2022
RAMA: Rapid algorithm for multicut problem

RAMA: Rapid algorithm for multicut problem Solves multicut (correlation clustering) problems orders of magnitude faster than CPU based solvers without

Paul Swoboda 60 Dec 13, 2022
PiRank: Learning to Rank via Differentiable Sorting

PiRank: Learning to Rank via Differentiable Sorting This repository provides a reference implementation for learning PiRank-based models as described

54 Dec 17, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

[CVPRW 2021] - Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

Anirudh S Chakravarthy 6 May 03, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022