Förderjahr 2020 / Project Call #15 / ProjektID: 5083 / Projekt: DETECT
Make sure to checkout the technical documentation and public gitlab repo for all the details on how to setup, develop and deploy the application "Fake-Shop Explorer"
Within the DETECT project we were able to successfully implement and release a prototype of the “Fake-Shop Explorer”. This browser-based game allows users to directly engage in the Fake-Shop Detection Life-Cycle i.e. assist the experts from Watchlist Internet in exposing fraudulent e-commerce sites by answering simple questions about suspected Fake-Shops in a meaningful but yet fun way. The public demonstrator is directly integrated with the AI powered Fake-Shop Detector API and Fake-Shop database (for further information on how to protect yourself in an easy, safe and reliable way see fakeshop.at).
Today we have released the technical user guide, containing all the information required to develop, build and deploy the “Fake-Shop Explorer”. The application is implemented as a Flask service, requires Node.js 16 and Python >= 3.9 and can be run in a containerised setup (using
docker-compose) or just locally without Docker. The software is available under the GPL 3.0 license. Please make sure to check out and join the public Gitlab Repo where we already have started to populate the issue tracker with items related to the milestone 2.0 release. We’re really looking forward to growing this community driven prevention approach together with you...
To get started, download the sources at git clone https://git-service.ait.ac.at/dsai-public/detect
Die Unit Data Science & Artificial Intelligence (DSAI) bietet Beratung im Bereich Data Science und entwickelt Lösungen, die eine fundierte Entscheidungsfindung auf Basis großer, heterogener Datensätze und Echtzeitdaten ermöglichen.