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

Area of Science:

• Computer Science
• Artificial Intelligence
• Digital Forensics

Background:

• Rapid advancements in deepfake technology create convincing fabricated media.
• Deepfakes pose significant risks to media integrity, political stability, and personal security.
• The authenticity of digital content is increasingly challenged by sophisticated manipulation techniques.

Purpose of the Study:

• To comprehensively survey and evaluate autonomous techniques for deepfake media identification.
• To analyze state-of-the-art methods for detecting manipulated image, audio, and video content.
• To enhance understanding of deepfake detection for improved digital ecosystem security.

Main Methods:

• In-depth analysis of deep learning models and other fundamental technologies for deepfake detection.
• Exploration of novel detection methods using machine learning, computer vision, and audio analysis.
• Review of recent research (2018-2024) on autonomous deepfake identification and evaluation.

Main Results:

• Identification of key deepfake detection techniques and tools.
• Evaluation of the efficacy of various methods in distinguishing real from manipulated media.
• Highlighting the latest developments in autonomous deepfake analysis.

Conclusions:

• Autonomous deepfake detection is critical for combating misinformation and ensuring digital content authenticity.
• Continued research in machine learning and AI is vital for staying ahead of deepfake evolution.
• Enhanced detection capabilities are essential for maintaining trust in the digital information landscape.