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Deepfakes in Social Engineering Attacks

  • Darren Steve Dsouza,
  • Ayman El Hajjar,
  • Hamid Jahankhani

摘要

This research delves extensively into deepfake technology's substantial influence on cybersecurity, revealing its subtle aspects and potential social repercussions. The investigation begins with deciphering the practical process for generating deepfakes using DeepFace Lab, providing light on every tedious step from data extraction to model training and video conversion. The study delves into the domain of social engineering attacks, explicitly depicting situations in which hostile actors use deepfakes to influence trust. In such instances, the combination of realistic visual and audio aspects raises the sophistication of cyber threats, needing more awareness and strong authentication protocols. The research delves further into the dark side of political manipulation, giving a clear picture of how perfectly constructed deepfakes may destabilise political landscapes. The hypothetical situation provided highlights the potential harm deepfakes can have on public trust and democratic processes, as well as the difficulty established media sources confront in real-time verification. The research closes by advocating for an initiative-taking and collaborative reaction by experts, legislators, and society to strengthen defences against the weaponization of fabricated media, emphasising the fragile equilibrium between technical advancement and the preservation of democratic principles.