PicHunt: Social Media Image Retrieval for Improved Law Enforcement
This addresses the need for first responders to efficiently analyze social media images that threaten public safety, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of retrieving similar images from social media to aid law enforcement in monitoring content that incites violence, by comparing hand-crafted features and a CNN model, which outperforms state-of-the-art hand-crafted features and reduces search space by 67% on average.
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%.