Year: 2025
Type: article
Abstract: : The rapid advancement of artificial intelligence has led to the emergence of highly realistic synthetic media, commonly known as deepfakes. While this technology offers creative potential, it also presents significant cybersecurity threats, including misinformation, identity theft, political manipulation, and fraud. This paper explores the application of AI-driven techniques for the detection of deepfakes as a critical component of modern cybersecurity. We review the state-of-the-art approaches in deepfake detection, focusing on deep learning models such as Convolutional Neural Networks (CNNs), Transformer architectures, and hybrid models that leverage visual and audio inconsistencies. The study also evaluates existing datasets and performance benchmarks, highlighting current limitations and challenges in real-world deployment. Our findings underscore the need for robust, explainable, and generalizable AI systems to combat the evolving threat of deepfakes and ensure digital media integrity. Furthermore, we emphasize the importance of evaluating dataset biases, adversarial threats to detection models, and the scalability of detection systems in real-world settings such as social media platforms. Future directions include the integration of AI with cryptographic verification, multimodal detection strategies, blockchain-based authentication, and real-time analysis tools for proactive defense against synthetic media attacks.
Authors: Dr. Sangeeta Joshi, Lalit Kumar Joshi
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410604668