Exploitation of Semantic Keywords for Malicious Event Classification

TL;DR

This paper empirically demonstrates that incorporating semantic keywords significantly improves the accuracy of classifying visually similar malicious events, using a novel dataset and attention-based models.

Contribution

It introduces a new dataset and shows how semantic keywords and attention models enhance malicious event classification accuracy.

Findings

Semantic keywords improve event discrimination.

Attention models focus on discriminant attributes.

Keyword-driven fusion boosts classification performance.

Abstract

Learning an event classifier is challenging when the scenes are semantically different but visually similar. However, as humans, we typically handle such tasks painlessly by adding our background semantic knowledge. Motivated by this observation, we aim to provide an empirical study about how additional information such as semantic keywords can boost up the discrimination of such events. To demonstrate the validity of this study, we first construct a novel Malicious Crowd Dataset containing crowd images with two events, benign and malicious, which look visually similar. Note that the primary focus of this paper is not to provide the state-of-the-art performance on this dataset but to show the beneficial aspects of using semantically-driven keyword information. By leveraging crowd-sourcing platforms, such as Amazon Mechanical Turk, we collect semantic keywords associated with images and then subsequently identify a subset of keywords (e.g. police, fire, etc.) unique to specific events. We first show that by using recently introduced attention models, a naive CNN-based event classifier actually learns to primarily focus on local attributes associated with the discriminant semantic keywords identified by the Turks. We further show that incorporating the keyword-driven information into early- and late-fusion approaches can significantly enhance malicious event classification.