International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
Exploring Vulnerabilities in Blockchain-Based Device Networks: How Hackers Can Exploit Computer Vision and Webcams to Gain Unauthorized Access and Compromise Security RACHIT DUA, Prof. SOUBHAGYA SANKAR BARPANDA Vellore Institute of Technology, AP (G-30, Inavolu, Beside AP Secretariat, Amaravati, Andhra Pradesh 522237, India.) ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – This paper investigates security weaknesses in networks of blockchain-connected devices, focusing on how malicious actors can exploit computer vision technologies and webcams to circumvent security protocols. Through thorough research and expert insights, we have discovered that while some systems successfully identify threats, a significant number remain vulnerable to cyber intrusions, which can result in unauthorized access and breaches of privacy.
While blockchain technology enhances security in digital transactions, its integration with vision-based systems introduces new vulnerabilities. Cybercriminals exploit weak passwords, unencrypted video feeds, and outdated software to gain unauthorized access, manipulate sensitive data, and breach security protocols. Additionally, AI-driven computer vision models face threats such as adversarial manipulation, data poisoning, and deepfake techniques, which can deceive AI models and bypass existing security mechanisms.[4]
The motivation for this research was prompted by a highly publicized post in which Mark Zuckerberg was seen obscuring his laptop’s webcam and microphone with tape, highlighting concerns over the dangers of webcam hacking. This incident led us to examine the scope of these vulnerabilities and to investigate effective protective measures.
1.1 Background and Motivation The 2014 Black-Shades attack highlighted how unsecured visual systems could be infiltrated for unauthorized surveillance and data theft, underscoring the need for improved protection. Although blockchain technology offers decentralized security and data integrity, its integration with computer vision systems introduces new vulnerabilities that are increasingly exploited by cybercriminals.[7]
To counter these threats, we suggest implementing an AIenhanced Intrusion Detection System (IDS) that utilizes machine learning to evaluate visual data, identify irregularities, and react to threats instantaneously. By securely recording security events on the blockchain, this system guarantees transparency, integrity, and responsibility. This research emphasizes the pressing need for strong security measures, ongoing surveillance, and heightened user awareness to safeguard decentralized vision-based systems, proposing practical strategies to reduce the risks of webcam hacking and privacy violations.
1.2 Problem Statement and Challenges Current security measures, including traditional Intrusion Detection Systems (IDS), rely on rule-based detection, which struggles to counter AI-driven cyber threats. These conventional methods fail against deepfake manipulations, adversarial attacks, and evolving cyber threats, leaving blockchain-connected vision systems exposed to potential breaches. Industries such as healthcare, finance, and smart surveillance are at significant risk due to these vulnerabilities. Despite blockchain’s decentralized security, its application in vision-based systems remains an overlooked and exploitable attack surface.
Keywords: Blockchain security, computer vision vulnerabilities, webcam exploitation, cybersecurity, decentralized networks, AI-driven intrusion detection systems.
1.INTRODUCTION Hacking has evolved from a curiosity-driven activity in the 1960s to a sophisticated cybercrime industry, exploiting technological advancements and security loopholes. By the 2000s, hackers increasingly targeted internet-connected devices, particularly webcams, using malware and remote access tools for unauthorized surveillance and data theft. The 2014 BlackShades attack exposed the severe risks of unsecured webcams, highlighting the urgent need for advanced protective measures.
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