Deepfake technology has been advancing rapidly, blurring the lines between what's real and what's fake. This has led to major concerns about the trustworthiness of digital content. Deepfakes involve altering images, audio, and videos to create convincing but entirely fake content. This poses serious threats to media, politics, and even personal safety. To tackle this growing issue, a thorough examination of autonomous methods for detecting and evaluating deepfakes has been conducted. This study focuses on the latest techniques and tools for spotting deepfakes in images, videos, and audio.

The core technologies behind these detection methods include deep learning models. These models are evaluated for their ability to distinguish between real and manipulated media. The study also explores advanced detection methods that use machine learning, computer vision, and audio analysis. All the research included in this study was conducted between 2018 and 2024, ensuring that the latest developments in the field are covered. In a world where telling fact from fiction is more important than ever, understanding and improving autonomous detection methods is crucial for enhancing digital security and awareness.

The study highlights the importance of staying ahead of deepfake technology. As deepfakes become more sophisticated, so must the methods used to detect them. This ongoing battle for truth in the digital age requires continuous innovation and vigilance. By advancing our understanding of autonomous detection methods, we can better protect the integrity of digital media and ensure that we can trust what we see and hear online.

Deepfakes: The Battle for Truth in the Digital Age

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