Text-to-3D Model Generation Accuracy: An Assessment of AI-Generated Visual Content in Unity Game Development Environments; Deepfake Deception: How AI-Enabled Social Engineering Reconstructed Corporate Authority Networks in the 2024 Arup Fraud 87 views

Cyberattackers exploit sociotechnical relationships between employees, technology, and corporate authority structures. Through biometric verification, multichannel confirmation protocols, and AI-powered forensic analysis, authentication systems detect and prevent deepfakes. Current detection tools analyze video and audio content for manipulated digital artifacts, yet organizations must balance security measures with operational efficiency. Success depends on implementing layered defenses with both technological solutions and human oversight. The Arup deepfake attackers succeeded by constructing a temporarily stable actor network that enrolled diverse participants into fraudulent relationships. In the terms of actor-network theory, the observed attackers functioned as network engineers, translating the interests of AI algorithms, corporate hierarchies, financial protocols, and trust relationships to orchestrate social engineering fraud. The analysis reveals that organizational vulnerabilities emerge from complex entanglements of human and non-human actors that attackers skillfully exploited rather than isolated technical or procedural failures.

BS (Bachelor of Science)

AI; Deepfake; Unity; 3D image generation; Artificial Intelligence; Cybersecurity; Cyberattack

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Haiying Shen STS Advisor: Peter Norton Technical Team Members: Shiyi Liu, Sebastian Shirazi

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2025-12-11