Deepfake Technology's Dual Nature: A Review of Security Risk Assessment and Defense Strategies

Deepfake technology, empowered by breakthroughs in deep learning-based image synthesis, is profoundly reshaping identity verification systems, finding extensive application in security, finance, and social media with enhanced convenience. However, its capacity to generate hyper-realistic facial forgeries presents a dual impact: while driving innovation, it simultaneously introduces unprecedented security threats, including privacy violations, identity spoofing, and data poisoning attacks. This paper systematically reviews and assesses current research progress on the security risks and defense strategies associated with Deepfake technology. Through synthesis of existing literature, this paper constructs a multidimensional analytical framework examining three core dimensions: first, the core technological principles underpinning Deepfakes and their evolution; second, the diverse spectrum of security risks arising from their misuse and their underlying mechanisms; and third, the effectiveness and inherent limitations of prevailing defense mechanisms, encompassing detection techniques and legal regulations. This study concludes that although Deepfakes advance facial recognition, mitigating their inherent security threats necessitates a multidimensional synergistic approach. This approach must integrate continuous technological advancements, robust legal oversight, and strengthened public awareness initiatives. Future efforts must prioritize establishing cross-disciplinary collaborative governance mechanisms to achieve a dynamic equilibrium between technological innovation and security assurance.