  First responders are increasingly using social media to identify and reduce
crime for well-being and safety of the society. Images shared on social media
hurting religious, political, communal and other sentiments of people, often
instigate violence and create law & order situations in society. This results
in the need for first responders to inspect the spread of such images and users
propagating them on social media. In this paper, we present a comparison
between different hand-crafted features and a Convolutional Neural Network
(CNN) model to retrieve similar images, which outperforms state-of-art
hand-crafted features. We propose an Open-Source-Intelligent (OSINT) real-time
image search system, robust to retrieve modified images that allows first
responders to analyze the current spread of images, sentiments floating and
details of users propagating such content. The system also aids officials to
save time of manually analyzing the content by reducing the search space on an
average by 67%.
