DEMIS: A Threat Model for Selectively Encrypted Visual Surveillance Data

TL;DR

This paper introduces DEMIS, a new threat model for analyzing security risks in selectively encrypted visual surveillance videos, and evaluates attack vectors and mitigation strategies through experiments on a custom dataset.

Contribution

It proposes the DEMIS threat model for analyzing threats against selectively encrypted videos and demonstrates attack simulations and mitigations using a novel dataset.

Findings

Selective encryption can still be vulnerable to various attacks.

The DEMIS model helps identify and analyze potential threats.

Experimental results show the severity of different attack types.

Abstract

The monitoring of individuals/objects has become increasingly possible in recent years due to the convenience of integrated cameras in many devices. Due to the important moments or activities of people captured by these devices, it has made it a great asset for attackers to launch attacks against by exploiting the weaknesses in these devices. Different studies proposed na\"ive/selective encryption of the captured visual data for safety but despite the encryption, an attacker can still access or manipulate such data. This paper proposed a novel threat model, DEMIS which helps analyse the threats against such encrypted videos. The paper also examines the attack vectors that can be used for threats and the mitigation that will reduce or prevent the attack. For experiments, firstly the data set is generated by applying selective encryption on the Regions-of-interests (ROI) of the tested videos using the image segmentation technique and Chacha20 cipher. Secondly, different types of attacks, such as inverse, lowercase, uppercase, random insertion, and malleability attacks were simulated in experiments to show the effects of the attacks, the risk matrix, and the severity of these attacks. Our developed data set with the original, selective encrypted, and attacked videos are available on git-repository(https://github.com/Ifeoluwapoo/video-datasets) for future researchers.