Advanced Plasmonic Resonance-enhanced Biosensor for Comprehensive Real-time Detection and Analysis of Deepfake Content

The rapid advancement of deep learning technologies has led to the proliferation of deepfake content, posing significant challenges for digital security, privacy, and the integrity of information. Traditional detection methods often struggle with real-time analysis and distinguishing sophisticated deepfakes. This study introduces an advanced plasmonic resonance-enhanced biosensor designed for comprehensive real-time detection and analysis of deepfake content, leveraging the unique properties of plasmonic materials to enhance sensitivity and accuracy. The biosensor system integrates plasmonic resonance techniques with machine learning algorithms to detect subtle anomalies in digital content. Plasmonic nanostructures are engineered to interact with specific optical signatures of authentic and manipulated media. The sensor’s response is captured and processed using a convolutional neural network (CNN) trained on a diverse dataset of real and deepfake images and videos. The system’s performance is evaluated based on detection accuracy, response time, and the ability to adapt to evolving deepfake techniques. The plasmonic resonance-enhanced biosensor demonstrated a significant improvement in detection capabilities compared to traditional methods. The system achieved an overall detection accuracy of 98.7%, with a false positive rate of 1.2% and a false negative rate of 0.5%. Real-time analysis showed an average response time of 0.8 s per frame, enabling efficient processing of video content. The adaptive learning capability of the CNN allowed the biosensor to maintain high accuracy even as new deepfake generation techniques were introduced. The advanced plasmonic resonance-enhanced biosensor presents a robust solution for real-time detection and analysis of deepfake content. Its high sensitivity and accuracy, coupled with rapid response times, make it an effective tool for safeguarding digital media integrity. Future work will focus on optimizing the sensor’s integration into various platforms and expanding its capabilities to detect a broader range of digital manipulations. This technology holds promise for enhancing security measures across multiple domains, including media verification, cybersecurity, and forensic analysis.