Hunt down social media accounts by username across social networks

Last update: Dec 14, 2021

Related tags

Deep Learning Sherlock

Overview

Hunt down social media accounts by username across social networks

Installation | Usage | Docker Notes | Contributing

Installation

# clone the repo
$ git clone https://github.com/sherlock-project/sherlock.git

# change the working directory to sherlock
$ cd sherlock

# install the requirements
$ python3 -m pip install -r requirements.txt

Usage

$ python3 sherlock --help
usage: sherlock [-h] [--version] [--verbose] [--folderoutput FOLDEROUTPUT]
                [--output OUTPUT] [--tor] [--unique-tor] [--csv]
                [--site SITE_NAME] [--proxy PROXY_URL] [--json JSON_FILE]
                [--timeout TIMEOUT] [--print-all] [--print-found] [--no-color]
                [--browse] [--local]
                USERNAMES [USERNAMES ...]

Sherlock: Find Usernames Across Social Networks (Version 0.14.0)

positional arguments:
  USERNAMES             One or more usernames to check with social networks.

optional arguments:
  -h, --help            show this help message and exit
  --version             Display version information and dependencies.
  --verbose, -v, -d, --debug
                        Display extra debugging information and metrics.
  --folderoutput FOLDEROUTPUT, -fo FOLDEROUTPUT
                        If using multiple usernames, the output of the results
                        will be saved to this folder.
  --output OUTPUT, -o OUTPUT
                        If using single username, the output of the result
                        will be saved to this file.
  --tor, -t             Make requests over Tor; increases runtime; requires
                        Tor to be installed and in system path.
  --unique-tor, -u      Make requests over Tor with new Tor circuit after each
                        request; increases runtime; requires Tor to be
                        installed and in system path.
  --csv                 Create Comma-Separated Values (CSV) File.
  --site SITE_NAME      Limit analysis to just the listed sites. Add multiple
                        options to specify more than one site.
  --proxy PROXY_URL, -p PROXY_URL
                        Make requests over a proxy. e.g.
                        socks5://127.0.0.1:1080
  --json JSON_FILE, -j JSON_FILE
                        Load data from a JSON file or an online, valid, JSON
                        file.
  --timeout TIMEOUT     Time (in seconds) to wait for response to requests.
                        Default timeout is infinity. A longer timeout will be
                        more likely to get results from slow sites. On the
                        other hand, this may cause a long delay to gather all
                        results.
  --print-all           Output sites where the username was not found.
  --print-found         Output sites where the username was found.
  --no-color            Don't color terminal output
  --browse, -b          Browse to all results on default browser.
  --local, -l           Force the use of the local data.json file.

To search for only one user:

python3 sherlock user123

To search for more than one user:

python3 sherlock user1 user2 user3

Accounts found will be stored in an individual text file with the corresponding username (e.g user123.txt).

Anaconda (Windows) Notes

If you are using Anaconda in Windows, using 'python3' might not work. Use 'python' instead.

Docker Notes

If docker is installed you can build an image and run this as a container.

docker build -t mysherlock-image .

Once the image is built, sherlock can be invoked by running the following:

docker run --rm -t mysherlock-image user123

The optional --rm flag removes the container filesystem on completion to prevent cruft build-up. See: https://docs.docker.com/engine/reference/run/#clean-up---rm

The optional -t flag allocates a pseudo-TTY which allows colored output. See: https://docs.docker.com/engine/reference/run/#foreground

Use the following command to access the saved results:

docker run --rm -t -v "$PWD/results:/opt/sherlock/results" mysherlock-image -o /opt/sherlock/results/text.txt user123

The -v "$PWD/results:/opt/sherlock/results" options tell docker to create (or use) the folder results in the present working directory and to mount it at /opt/sherlock/results on the docker container. The -o /opt/sherlock/results/text.txt option tells sherlock to output the result.

Or you can use "Docker Hub" to run sherlock:

docker run theyahya/sherlock user123

Using `docker-compose`

You can use the docker-compose.yml file from the repository and use this command:

docker-compose run sherlock -o /opt/sherlock/results/text.txt user123

Contributing

We would love to have you help us with the development of Sherlock. Each and every contribution is greatly valued!

Here are some things we would appreciate your help on:

Addition of new site support ¹
Bringing back site support of sites that have been removed in the past due to false positives

[1] Please look at the Wiki entry on adding new sites to understand the issues.

Tests

Thank you for contributing to Sherlock!

Before creating a pull request with new development, please run the tests to ensure that everything is working great. It would also be a good idea to run the tests before starting development to distinguish problems between your environment and the Sherlock software.

The following is an example of the command line to run all the tests for Sherlock. This invocation hides the progress text that Sherlock normally outputs, and instead shows the verbose output of the tests.

$ cd sherlock/sherlock
$ python3 -m unittest tests.all --verbose

Note that we do currently have 100% test coverage. Unfortunately, some of the sites that Sherlock checks are not always reliable, so it is common to get response problems. Any problems in connection will show up as warnings in the tests instead of true errors.

If some sites are failing due to connection problems (site is down, in maintenance, etc) you can exclude them from tests by creating a tests/.excluded_sites file with a list of sites to ignore (one site name per line).

Hunt down social media accounts by username across social networks

Related tags

Overview

Installation

Usage

Anaconda (Windows) Notes

Docker Notes

Using `docker-compose`

Contributing

Tests

Stargazers over time

License

Owner

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Hunt down social media accounts by username across social networks

Related tags

Overview

Installation

Usage

Anaconda (Windows) Notes

Docker Notes

Using docker-compose

Contributing

Tests

Stargazers over time

License

Owner

Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''.

The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble

Spatial Intention Maps for Multi-Agent Mobile Manipulation (ICRA 2021)

Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"

Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Implementation of Google Brain's WaveGrad high-fidelity vocoder

Additional environments compatible with OpenAI gym

End-to-End Referring Video Object Segmentation with Multimodal Transformers

Optimal space decomposition based-product quantization for approximate nearest neighbor search

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Code for our paper "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", ACL 2021

This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

[ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

Text-to-Image generation

code for Multi-scale Matching Networks for Semantic Correspondence, ICCV

On the adaptation of recurrent neural networks for system identification

Using `docker-compose`