Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection

2026-05-20 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

Researchers have developed a hybrid digital-analog deepfake video detection system that integrates a lightweight digital front-end with a spatially multiplexed optical decoding back-end. This architecture enables massively parallel analog inference using a programmable spatial light modulator, allowing the simultaneous processing of 15 or more video streams in a single optical pass. The system achieves high-throughput and accurate video-level authenticity prediction while significantly reducing computational costs compared to purely digital methods. Validated across various datasets, including face-swapping and AI-generated videos, the experimental setup demonstrated an average deepfake detection accuracy of 97.79%, sensitivity of 99.86%, and specificity of 95.72% on the Celeb-DF video dataset. This optical-neural approach also exhibits resilience against video degradation, noise, compression, experimental misalignments, and black-box adversarial attacks, offering simultaneous gains in throughput, energy efficiency, and adversarial robustness.

Key takeaway

For research scientists developing deepfake detection systems, this hybrid optical-neural architecture presents a compelling alternative to purely digital methods. You should consider integrating optical computation to achieve superior throughput, energy efficiency, and adversarial robustness, especially for high-volume video processing applications. This approach could significantly improve the scalability and resilience of your detection frameworks against evolving AI-generated threats.

Key insights

A hybrid optical-neural architecture significantly enhances deepfake detection throughput, energy efficiency, and robustness.

Principles

• Optical computing boosts AI inference.
• Multiplexing enables parallel video processing.

Method

The system combines a digital front-end with a spatially multiplexed optical decoding back-end, using a programmable spatial light modulator for parallel analog inference of 15+ video streams in one optical pass.

In practice

• Process 15+ video streams simultaneously.
• Enhance deepfake detection robustness.
• Reduce computational costs for video analysis.

Topics

• Deepfake Detection
• Optical Computing
• Hybrid Digital-Analog
• Spatial Light Modulator
• Energy Efficiency

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.