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

Lane assist for ETS2, built with the ultra-fast-lane-detection model.

Source code for "OmniPhotos: Casual 360° VR Photography"

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

Data and extra materials for the food safety publications classifier

DA2Lite is an automated model compression toolkit for PyTorch.

Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

[ICCV 2021 Oral] Deep Evidential Action Recognition

Implementation of FitVid video prediction model in JAX/Flax.

Yet another video caption

Code release for Convolutional Two-Stream Network Fusion for Video Action Recognition

Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

People Interaction Graph

Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)

This is a classifier which basically predicts whether there is a gun law in a state or not, depending on various things like murder rates etc.

Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler

Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

A self-supervised 3D representation learning framework named viewpoint bottleneck.

Public repository created to store my custom-made tools for Just Dance (UbiArt Engine)

[ICCV 2021] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

The official repository for "Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds"

Using `docker-compose`