Navigating Deepfakes with Data Science: A Multi-Modal Analysis and Blockchain-Based Detection Framework
Shalmali Patil, Aparna Krishna Bhat, Nilesh Jain, Vishal Javalkar · 2025
Deepfake technology, fueled by advancements in artificial intelligence, presents a dual-use phenomenon with innovative applications in entertainment, education, and communication alongside significant ethical and societal risks, such as misinformation, identity theft, and privacy violations. This paper proposes a novel framework combining multi-modal analysis and blockchain verification to enhance deepfake detection. The multi-modal approach employs audio-visual analysis, temporal and spatial inconsistencies, and advanced feature extraction techniques to identify manipulations, while blockchain provides a tamper-proof mechanism for authenticating digital content at its source. By addressing research gaps such as cross-domain adaptability and limited training datasets, the framework offers a holistic solution to escalating challenges. Moreover, the paper emphasizes the importance of transparency, privacy preservation, and global collaboration to align technological innovation with ethical accountability. This work contributes to building a trustworthy digital ecosystem, enabling responsible applications of deepfake technology while mitigating its risks.