  AI-generated synthetic media, also called Deepfakes, have significantly
influenced so many domains, from entertainment to cybersecurity. Generative
Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks
used to create Deepfakes, producing highly realistic yet fabricated content.
While these technologies open up new creative possibilities, they also bring
substantial ethical and security risks due to their potential misuse. The rise
of such advanced media has led to the development of a cognitive bias known as
Impostor Bias, where individuals doubt the authenticity of multimedia due to
the awareness of AI's capabilities. As a result, Deepfake detection has become
a vital area of research, focusing on identifying subtle inconsistencies and
artifacts with machine learning techniques, especially Convolutional Neural
Networks (CNNs). Research in forensic Deepfake technology encompasses five main
areas: detection, attribution and recognition, passive authentication,
detection in realistic scenarios, and active authentication. Each area tackles
specific challenges, from tracing the origins of synthetic media and examining
its inherent characteristics for authenticity. This paper reviews the primary
algorithms that address these challenges, examining their advantages,
limitations, and future prospects.
