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<p>In recent years, the proliferation of deepfake images has posed a substantial threat to media credibility, security, and privacy. Contemporary detection techniques, predominantly reliant on deep learning algorithms, fail to identify the nuanced pixel-level discrepancies inherent in deepfake material. This study introduces PlasmoVision, an innovative quantum-enhanced plasmonic imaging technology that incorporates AI-driven deep learning for highly sensitive real-time deepfake detection. Deepfakes alter digital images and videos to produce very persuasive fraudulent content, rendering traditional detection methods ineffective. Plasmonic surface resonance technology, in conjunction with quantum dots, has the capacity to capture intricate image features that can disclose such alterations. Integrating deep learning into this detection system improves the accuracy and velocity of analysis. The PlasmoVision technology employs quantum dot-enhanced plasmonic arrays to detect sub-pixel-level resonance shifts resulting from light interaction with the image surface. The optical signals are analyzed with a sophisticated convolutional neural network (CNN) that categorizes images according to the plasmonic resonance data. The AI model is trained on a varied dataset of genuine and deepfake photos, attaining an ideal equilibrium between detection sensitivity and speed. Real-time picture analysis is accomplished by swift plasmonic scanning and AI-driven classification. The suggested device attained an accuracy rate of 98.6% in identifying deepfakes within a test dataset, exhibiting a false positive rate of 1.2% and a false negative rate of 0.5%. The quantum-enhanced plasmonic system identified pixel abnormalities with a sensitivity of up to 10&#xa0;nm, markedly surpassing conventional deepfake detection technologies. PlasmoVision real-time analysis capacity decreased processing time by 35% relative to traditional approaches, rendering it exceptionally appropriate for extensive and real-time applications. The amalgamation of quantum dot plasmonic sensing and AI-driven deep learning in PlasmoVision provides an innovative solution for the precise and instantaneous identification of deepfake images. The device’s elevated sensitivity, swift detection capability, and minimal error rates represent a notable progression in picture authentication, offering strong protection against deepfake alterations across multiple sectors, including digital media and biometric security systems.</p>

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Innovative Quantum PlasmoVision-Based Imaging for Real-Time Deepfake Detection

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • R. Uma Maheshwari,
  • Jayasudha A.R,
  • Binay Kumar Pandey,
  • Digvijay Pandey

摘要

In recent years, the proliferation of deepfake images has posed a substantial threat to media credibility, security, and privacy. Contemporary detection techniques, predominantly reliant on deep learning algorithms, fail to identify the nuanced pixel-level discrepancies inherent in deepfake material. This study introduces PlasmoVision, an innovative quantum-enhanced plasmonic imaging technology that incorporates AI-driven deep learning for highly sensitive real-time deepfake detection. Deepfakes alter digital images and videos to produce very persuasive fraudulent content, rendering traditional detection methods ineffective. Plasmonic surface resonance technology, in conjunction with quantum dots, has the capacity to capture intricate image features that can disclose such alterations. Integrating deep learning into this detection system improves the accuracy and velocity of analysis. The PlasmoVision technology employs quantum dot-enhanced plasmonic arrays to detect sub-pixel-level resonance shifts resulting from light interaction with the image surface. The optical signals are analyzed with a sophisticated convolutional neural network (CNN) that categorizes images according to the plasmonic resonance data. The AI model is trained on a varied dataset of genuine and deepfake photos, attaining an ideal equilibrium between detection sensitivity and speed. Real-time picture analysis is accomplished by swift plasmonic scanning and AI-driven classification. The suggested device attained an accuracy rate of 98.6% in identifying deepfakes within a test dataset, exhibiting a false positive rate of 1.2% and a false negative rate of 0.5%. The quantum-enhanced plasmonic system identified pixel abnormalities with a sensitivity of up to 10 nm, markedly surpassing conventional deepfake detection technologies. PlasmoVision real-time analysis capacity decreased processing time by 35% relative to traditional approaches, rendering it exceptionally appropriate for extensive and real-time applications. The amalgamation of quantum dot plasmonic sensing and AI-driven deep learning in PlasmoVision provides an innovative solution for the precise and instantaneous identification of deepfake images. The device’s elevated sensitivity, swift detection capability, and minimal error rates represent a notable progression in picture authentication, offering strong protection against deepfake alterations across multiple sectors, including digital media and biometric security systems.