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CHEST 2021 Research Project Abstracts

P24_21: Secure Cloud-based Image Fusion, Analysis and Visualization Platform for Security Assessment of Electronics
Topic Areas: AFRL Topics
Principal investigator: Dr. Sina Shahbazmohamadi, University of Connecticut
PI Email

Investigation of electronics for hardware security assessment, counterfeit detection, reverse engineering, and attack simulation requires utilization of information from numerous instrumentation and imaging modalities. Such multimodal information either in the form of images or spectral data need to be spatially correlated and fused to augment our understanding beyond what each instrumentation offers in isolation from other instruments. A reliable image/information fusion process can significantly enhance the efficiency of the investigation, facilitate localization of region of interest. In addition, it enables us to leverage recent advances in machine learning to perform faster imaging and/analysis and take steps toward automation to reduce subjectivity in our investigation. However, to be practical and adoptable in real life and day to day industrial applications, several challenges need to be addressed: First, there needs to be a platform where subject matter experts can interact and provide input to be fed into machine learning algorithms as training data. Such platform should be able to visualize and correlate multimodal and multi-dimensional data. Another challenge is the size and complexity of the data which makes visualization, analysis and interactive investigation even more difficult. Our solution proposes a secure cloud-based software system that will automate the correlation of data and process the analytical information from any imaging and spectroscopy platforms to submicron resolution for a high-degree of accuracy using machine learning practices. Spectral data will be spatially resolved and annotated on each pixel of the image files. The higher resolution data will be used as training data for the machine learning algorithm to correct aberrations and restore/enhance image quality in lower resolution platform-specific images. The cloud platform enables scalability and allows for collaborative and interactive environment however safeguards will be implemented for end-to-end encryption of data to ensure data security.