Potential of artificial intelligence in deepfake media: From generation to detection mechanisms, state-of-the-art, and challenges

Artificial intelligence (AI) plays an important role in the generation of deepfakes by leveraging advanced machine learning models to create hyper-realistic synthetic media across visual, audio, and multimodal formats. The rapid evolution of deepfake technologies, alongside the exponential growth of digital media, demands a comprehensive and critical examination of current capabilities and challenges. Although the concept of media manipulation is not new, the sophistication and accessibility of AI-driven deepfakes present significant threats of misinformation to society and hence cause societal manipulation. This manuscript presents a systematic review of deepfake generation and detection techniques from 2017 to 2025, highlighting the progression of generative models and evaluating detection strategies. The main focus of this work is on the state-of-the-art (SOTA) techniques using adversarial networks, vision transformers (ViTs), attention mechanisms, hybrid learning frameworks, and ensemble models. The study thoroughly examines the benefits and drawbacks of existing methods. It also points out how vulnerable detection systems are to adversarial attacks and compares modern methods with traditional forensic and heuristic approaches. The paper critically analyzes the strengths and limitations of existing models, underscores the susceptibility of detection systems to adversarial attacks, and contrasts contemporary approaches with traditional forensic and heuristic-based methods. In addition to technical insights, the review puts a major focus on practical concerns such as scalability, regulatory frameworks, and the broader societal impact of the deepfake technology. By including benchmark datasets, standard tools, performance evaluation metrics, and relevant policy discussions, the manuscript presents a forward-looking perspective on the ongoing arms race between deepfake generation and detection. The study ends by highlighting the need for strong, flexible, and understandable detection systems, backed by effective policy measures, to reduce the growing risks posed by deepfakes.