ENHANCING DIGITAL TRUST: DETECTING DEEPFAKES USING DEEP LEARNING

Abstract: The growing sophistication of deepfake technology has created serious challenges for digital forensics, particularly within law enforcement. Deepfakes highly convincing but entirely fabricated audio, video, and image content—pose significant threats to public trust, security, and the integrity of investigations. To counter these risks, this project proposes the development of a unified software solution designed to detect deepfakes across multiple media formats, tailored specifically for cyber police use. The system will combine Machine Learning, Artificial Intelligence, and forensic analysis techniques to uncover tampering and manipulation in digital content. With a multi-layered detection framework, it will analyze visual anomalies and audio inconsistencies, employing Deep Learning models such as CNNs for image and video analysis and RNNs for audio detection to distinguish between genuine and fake media. Trained on extensive datasets, the system enhances detection accuracy and strengthens the fight against digital deception. Furthermore, it will seamlessly integrate with existing forensic tools, empowering investigators to quickly assess the authenticity of digital evidence. This advanced detection platform is intended to aid law enforcement in preventing and investigating crimes involving fraud, identity theft, blackmail, and the spread of misinformation enabled by deepfake technology.

Keywords: Deepfake Detection, Digital Forensics, Convolutional Neural Networks (CNNs), Reccurent Neural Networks (RNNs)