AI-Generated Deepfakes for Cyber Fraud and Detection

The fast-evolving strides of artificial intelligence, specifically using generative adversarial networks (GANs), have ushered in the era of deepfakes-artificial media capable of replicating human faces, voices, and actions with comparative ease.Although the technology has revolutionary and positive applications across the domains of filmmaking and accessibility, it equally bears colossal risks if used for cyber fraud.Deepfakes are being used more and more by cybercriminals for impersonation, identity theft, business email compromise (BEC), and various other types of deception.Impersonation of CEOs over video calls, audio message fakes to approve illegal fund transfers, and evading biometric security controls using synthetic faces and voices with a hyper-realistic appearance are now achievable by fraudsters.The existing reality of deepfakes as a tool for cybercrime is examined in this paper.It discusses actual events where deepfakes were utilized to take advantage of, deceive, or financially exploit individuals and groups.Moreover, it has a detailed description of the detection methods created to help counter this emerging threat.These include some of them being passive detection methods like artifact and frequency analysis, deep learning classifiers, and biological signal detection, and others being active detection methods like liveness checks, watermarking, and blockchain-based content verification.Despite concerted efforts, the race between deepfake generation and detection remains on an upward trajectory.Attackers continue to adapt to remain undetected, and conventional forensic mechanisms become less effective with time.The paper concludes on a note highlighting the importance of hybrid detection systems, robust regulatory frameworks, and global cooperation to enable ethical and secure use of AI-generated content.