QR2Pass-project - A proof of concept for an alternative (passwordless) authentication system to a web server

Overview

QR2Pass

This is a proof of concept for an alternative (passwordless) authentication system to a web server. The authentication is based on public key cryptographic challenges, that can correctly responded only by the owner of the private key. Challenges are presented in the form of a QR code which are scanned by the mobile app.

The project is based on the procedure proposed by the Snap2Pass paper, but not on the corresponding implementation. In contrast to Snap2Pass, it offers only public key authentication (i.e no shared secret) and there is no OpenID integration.

The server is written in Django and the client (mobile app) is written in Swift for the iOS platform

You can check an online version of the server here

Overview

During registration, user provides their public key to the server. For authentication, server presents a challenge (unique nonce that expires after 60 seconds). User needs to sign the challenge with their private key part. Server verifies the signature and if it's valid, user is authenticated into the web site.

The web app consists of 2 parts:

  • the core app that handles the web view (what users sees in their browser)
  • the api app that handles the out-of-band communication (to/from the mobile app)

Protocol overview

To complete the registration request, or to initate a login process, the web app (core) constructs QR codes that are scanned by the mobile app

register QR

the registration QR has the following info:

   {
       "version": Int, 
       "email": String, 
       "nonce": String,
       "provider": URL, 
       "respond_to": URL,
       "action": action enum //action.register 
   }
  • version: version of the prorocol (currently ignored)
  • email: the email provided in the registration form. It is currently used as a user identifier
  • nonce: a unique nonce (used to avoid replay attacks)
  • provider: base url for the site (this is the identifier for the site)
  • respond_to: where the client should send its response
  • action: either login or register (register in this case, duh!)

login QR

the login QR has a very similar schema:

    {
        "version": Int,
        "challenge": String,
        "validTill": Date, 
        "provider": URL, 
        "respond_to": URL,
        "action": action.login //action.login 
    }

email, is not provided by the server, but in the client's request (from the mobile app)

Out of band requests/responses

We define as out-of-band the requests between the mobile app and the server (api part) Browser - server (core part) is in-band

Registration

A user needs first to head to the registration page (in their browser) where they are asked for their email. If the email is valid and not already used, a registration QR code is presented (for 60 seconds). The user uses the mobile app to scan the QR code.
The app decodes the QR code (see register schema above) and extracts the URL from the "respond_to field"
If there is no registration data in the app for this site (defined by the "provider" field), it will then send a register request to this URL using the following schema:

    {
        "version": Int,
        "email": String,
        "public_key": String, 
        "nonce": String 
    }
  • version: version of the prorocol (currently ignored)
  • email: the user's email
  • public_key: the user's public key
  • nonce: the nonce offered by the server

Upon receiving the request, the server will perform the following checks:

  • request has the valid schema
  • the nonce received is a valid one and has not expired.
  • the nonce received, corresponds to the specific user.

If the checks are succesful, server creates a user in its DB and redirects the browser to login page

Server responds using the following schema (out-of-band):

    {
        "version": Int,
        "email": String,
        "status": String, 
        "response_text": String 
    }
  • status: "ok"/"nok"
  • response_text: a message showing more info about the status (e.g "invalid token")

Loging in

A previously registered user can head to the login page to log in. A QR is presented (for 60 seconds) The user uses the mobile app to scan the QR code.
The app decodes the QR code (see login schema above) and extracts the URL from the "respond_to field".
If there is registration data in the app for this site (defined by the "provider" field), it will then send a register request to this URL using the following schema:

{

    "version": Int,
    "username": String,
    "challenge": String, 
    "response": String 

}
  • username: the email of the user
  • challenge: the nonce provided by the server
  • response: the nonce signed by the private key of the user

Similarly to registration process, server will make some initial checks on the request (valid schema and nonce, etc). If the intial checks succeed, the signed challenge will be checked against the public key of the user (stored during the registration process). If all checks are succesful, user is authenticated in the backend and the browser will be redirected to the user page.

Server responds to the app with a repsonse using the same response schema as the in the registration process

Running the project

Client

The ios app doesn't use any external libraries and it is compatible to ios > 12.4
Keep in mind that iOS won't accept initiating unsecure connections (plain HTTP). See here for more information and ways to circumvent that, in case you want to test this locally.
Alternatively, you can use ngrok to map an external https endpoint to your local machine

Server

pre-requisites

The server uses redis for Django channels backend and for temporary storage (nonces), so you need to have redis running locally or remotely.
It also uses daphne as an asynchronous server. You can invoke daphne by running:

daphne qr2pass.asgi:application --port <PORT> --bind 0.0.0.0 -v2

but locally you can also use the usual runserver command:

python manage.py runserver

requirements

  • create a virtual environment
  • activate it
  • pip3 install -r requirements.txt

Settings

The default settings are defined in the settings/defaults.py file.
You need to fill in some additional settings corresponding to your deployment environment (see deployment-template.py) and define the DJANGO_SETTINGS_MODULE environmental variable for details) to point to your settings (see here)

An official PyTorch implementation of the TKDE paper "Self-Supervised Graph Representation Learning via Topology Transformations".

Self-Supervised Graph Representation Learning via Topology Transformations This repository is the official PyTorch implementation of the following pap

Hsiang Gao 2 Oct 31, 2022
People movement type classifier with YOLOv4 detection and SORT tracking.

Movement classification The goal of this project would be movement classification of people, in other words, walking (normal and fast) and running. Yo

4 Sep 21, 2021
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
In this project, we'll be making our own screen recorder in Python using some libraries.

Screen Recorder in Python Project Description: In this project, we'll be making our own screen recorder in Python using some libraries. Requirements:

Hassan Shahzad 4 Jan 24, 2022
The Noise Contrastive Estimation for softmax output written in Pytorch

An NCE implementation in pytorch About NCE Noise Contrastive Estimation (NCE) is an approximation method that is used to work around the huge computat

Kaiyu Shi 287 Nov 25, 2022
Code for Temporally Abstract Partial Models

Code for Temporally Abstract Partial Models Accompanies the code for the experimental section of the paper: Temporally Abstract Partial Models, Khetar

DeepMind 19 Jul 13, 2022
T2F: text to face generation using Deep Learning

⭐ [NEW] ⭐ T2F - 2.0 Teaser (coming soon ...) Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN

Animesh Karnewar 533 Dec 22, 2022
Recognize numbers from an (28 x 28) image using neural networks

Number recognition Recognize numbers from a 28 x 28 image using neural networks Usage This is an example of a simple usage of number-recognition NOTE:

Mauro Baladés 2 Dec 29, 2021
Official PyTorch implementation of the NeurIPS 2021 paper StyleGAN3

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Eugenio Herrera 92 Nov 18, 2022
PAIRED in PyTorch 🔥

PAIRED This codebase provides a PyTorch implementation of Protagonist Antagonist Induced Regret Environment Design (PAIRED), which was first introduce

UCL DARK Lab 46 Dec 12, 2022
Pytorch implementation of VAEs for heterogeneous likelihoods.

Heterogeneous VAEs Beware: This repository is under construction 🛠️ Pytorch implementation of different VAE models to model heterogeneous data. Here,

Adrián Javaloy 35 Nov 29, 2022
LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

Query Selector Here you can find code and data loaders for the paper https://arxiv.org/pdf/2107.08687v1.pdf . Query Selector is a novel approach to sp

MORAI 62 Dec 17, 2022
SemEval2022 Patronizing and Condescending Language (PCL) Detection

SemEval2022 Patronizing and Condescending Language (PCL) Detection This task is from SemEval 2022. What is Patronizing and Condescending Language (PCL

Daniel Saeedi 0 Aug 05, 2022
An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Named-Entity-Recognition-NER-Papers by Pengfei Liu, Jinlan Fu and other contributors. An elaborate and exhaustive paper list for Named Entity Recognit

Pengfei Liu 388 Dec 18, 2022
pytorch bert intent classification and slot filling

pytorch_bert_intent_classification_and_slot_filling 基于pytorch的中文意图识别和槽位填充 说明 基本思路就是:分类+序列标注(命名实体识别)同时训练。 使用的预训练模型:hugging face上的chinese-bert-wwm-ext 依

西西嘛呦 33 Dec 15, 2022
Code for "Reconstructing 3D Human Pose by Watching Humans in the Mirror", CVPR 2021 oral

Reconstructing 3D Human Pose by Watching Humans in the Mirror Qi Fang*, Qing Shuai*, Junting Dong, Hujun Bao, Xiaowei Zhou CVPR 2021 Oral The videos a

ZJU3DV 178 Dec 13, 2022
AI-Bot - 一个基于watermelon改造的OpenAI-GPT-2的智能机器人

AI-Bot 一个基于watermelon改造的OpenAI-GPT-2的智能机器人 在Binder上直接运行测试 目前有两种实现方式 TF2的GPT-2 TF

9 Nov 16, 2022
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation Learning (CVPR 2022 Oral) 2022-03-29: The paper was selected as a CVPR 2022 Oral paper! 2

249 Dec 28, 2022
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN in PyTorch PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in READM

Taehoon Kim 1k Jan 04, 2023
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

TorchMultimodal (Alpha Release) Introduction TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

Meta Research 663 Jan 06, 2023