PicHunt: Social Media Image Retrieval for Improved Law Enforcement

TL;DR

This paper introduces PicHunt, an open-source real-time image retrieval system using CNNs to help law enforcement quickly identify and analyze the spread of harmful images on social media, reducing manual effort significantly.

Contribution

It compares hand-crafted features with CNN models for image retrieval and develops a robust system for law enforcement to monitor social media content efficiently.

Findings

CNN-based features outperform hand-crafted features in image retrieval.

The system reduces manual analysis time by an average of 67%.

It effectively retrieves modified images and analyzes content spread.

Abstract

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%.