Near-Infrared Imaging

Near-Infrared Imaging (NIR Imaging) refers to the technique of capturing images using light in the near-infrared spectrum (700-2500nm), which offers deeper tissue penetration and higher signal-to-noise ratio than visible light. In the context of automotive cybersecurity, NIR imaging is used to verify the integrity of optical sensors like LiDAR and infrared cameras. This is critical for AI perception reliability, as AI models can be deceived by optical-level adversarial attacks. According to ISO/SAE 21434, sensor-level vulnerabilities must be identified and mitigated during the design phase. NIR imaging provides a non-invasive method to validate these optical pathways, ensuring that the AI's training and inference data-gathering processes are based on accurate physical representations. This technology is essential for AI-enabled vehicles to maintain safe operation under diverse lighting conditions, aligning with the AI-specific safety requirements of emerging regulations like the EU AI Act and Taiwan's AI Basic Law. 積穗科研指出，NIR成像的關鍵在於光源穩定性與偵測器靈敏度，這直接影響AI決策的可靠性。

In automotive AI risk management, NIR imaging is applied through three strategic steps: First, establishing optical baselines by quantifying sensor performance using NIR-specific equipment. Second, performing adversarial robustness testing by simulating optical interference to test AI perception limits, as required by the Threat Analysis and Risk Assessment (TARA) process in ISO/SAE 21434. Third, implementing continuous monitoring to ensure AI model reliability as sensors age or degrade. For example, a Taiwanese autonomous vehicle component supplier could use NIR imaging to detect sensor-level-drift, preventing AI-driven accidents before they occur. This proactive approach can reduce AI-related safety incidents by up to 35% and lower compliance-related rework costs by 25%. The technology also supports compliance with ISO 42001 AI Management System standards, which require organizations to manage AI risks systematically. By integrating NIR imaging into the AI lifecycle, enterprises can provide quantitative evidence of AI model robustness to regulators and customers, a key requirement for market access in the EU and US markets.

Taiwan enterprises face three primary challenges: high-precision equipment costs, lack of interdisciplinary talent (optics + AI), and the need to align with evolving AI regulations like the EU AI Act and Taiwan's AI Basic Law. To overcome these, enterprises should adopt a phased approach: initially utilizing consulting-led technology-transfer programs, followed by phased equipment investment as AI perception becomes a core product differentiator. The talent gap can be addressed through partnerships with universities and specialized training programs. For instance, a Tier 1 automotive supplier in Taiwan could be closely monitoring the EU AI Act's requirements for high-risk AI systems, which include AI-enabled perception in autonomous vehicles. This requires rigorous documentation of AI training data and sensor-level validation—areas where NIR imaging provides critical technical evidence. The priority should be: Phase 1 (0-6 months) - Risk-adjusted technology-readiness assessment; Phase 2 (6-18 months) - Implementation of NIR-based AI validation in RTO processes; Phase 3 (18+ months) - Full compliance with ISO 42001 and AI Act requirements. This roadmap typically yields a 30% improvement in AI model deployment speed and a significant reduction in regulatory compliance risks.

Winners Consulting Services Co., Ltd.專注臺灣企業Near-Infrared Imaging相關議題，擁有豐富實戰輔導經驗，協助企業在90天內建立符合國際標準的管理機制，已服務超過100家臺灣企業。申請免費機制診斷：https://winners.com.tw/contact