The Science of Deepfakes and Digital Ethics: A Comparative Review of Generation Techniques, Detection Methods, and Professional Responsibility

Deepfakes – synthetic images, audio, and video produced with deep generative models – have rapidly transitioned from an isolated laboratory curiosity to a mainstream technical and socio-ethical concern. This review paper presents a structured, in-depth evaluation of deepfake generation and detection techniques published from 2021 to 2026, documenting the paradigm shift from early Generative Adversarial Network (GAN)-based pipelines to diffusion models and multi-modal generative systems. We compile the technical literature into a multi-dimensional comparison framework covering generation approaches, detection strategies, operational strengths, and core limitations, summarizing these metrics within a comparative taxonomy table. Moving beyond technical mechanics, we connect these advancements to the professional-ethics domain, leveraging the analytical framework of A Gift of Fire by Sara Baase to dissect challenges surrounding individual privacy, informed consent, diffuse accountability, and the ethical mandates of computing professionals. We further survey the changing global regulatory landscape, assessing the enforcement mechanisms of the European Union AI Act alongside fragmented state- and federal-level legislative efforts within the United States. Our findings show that forensic detection architectures consistently lag behind generative capability, as demonstrated by severe accuracy drops across recent in-the-wild multi-modal benchmarks. We conclude that addressing this socio-technical vulnerability requires a coordinated strategy combining generalized multi-modal detection architectures with proactive ethics-by-design development frameworks and harmonized international policy.