Build a "Spam Buster" for user generated IMAGE content based on predefined text, links identified as "known bads", based on which the image should be classified as spam or not-spam. The "known bads" can be updated anytime. The "Spam Buster" would need to analyze at image level, identify text components inside image and build appropriate logic to give a score to the image. A similar use case is employed by Facebook Ads technology to identify if the image ad contains too much text or contains any objectionable text."

We build a flask based micro-service which gives a "Spaminess score" for any uploaded image. The score is derived by aggregating the scores from two models:

1. Text based spam classifier - Text is extracted from the image and a pre-trained model runs on the text to classify text in respective bucket
2. Image based spam classifier - Convolution neural network which classifies image as spam or not based on the image features

Image to Text

Image Spam Classifier

Text Spam Classifier

Clone the repo and cd into predict folder

cd predict

pip install -r requirements.txt

python3 app.py

Clone the repo and cd into predict folder

Run the image and bash into it to start the server

docker run --rm -itd --name hack -v /home/mayur/hack/spam_buster/predict:/predict -p 5000:5000 kayush206/img_spam:v2 bash

docker exec -it hack bash

cd /predict

python3 app.py

Access the service- http://localhost:5000/spam_buster/api/v1/model

Members

• Mayur Madnani
• Ayush Kumar
• Shweta Pandey