Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation

The fast progress of deepfake technology has caused a huge overlap between reality and deceit, leading to substantial worries over the authenticity of digital media content. Deepfakes, which involve the manipulation of image, audio and video to produce highly convincing yet completely fabricated content, present significant risks to media, politics, and personal well-being. To address this increasing problem, our comprehensive survey investigates the advancement along with evaluation of autonomous techniques for identifying and evaluating deepfake media. This paper provides an in-depth analysis of state-of-the-art techniques and tools for identifying deepfakes, encompassing image, video, and audio-based content. We explore the fundamental technologies, such as deep learning models, and evaluate their efficacy in differentiating real and manipulated media. In addition, we explore novel detection methods that utilize sophisticated machine learning, computer vision, and audio analysis techniques. The study we conducted included exclusively the most recent research conducted between 2018 and 2024, which represents the newest developments in the area. In an era where distinguishing fact from fiction is paramount, we aim to enhance the security and awareness of the digital ecosystem by advancing our understanding of autonomous detection and evaluation methods.