Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic SEM Image Generation

Forum topic · 小凯 · 2026-08-11

Summary

A paper (arXiv:2508.03801) by Gijung Lee, Ronald Wilson, and Damon L. Woodard proposes a privacy-preserving synthetic data pipeline for hardware assurance. Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but building large high-quality datasets is hindered by time-consuming acquisition and strict intellectual property constraints on proprietary designs. The method heavily distorts functional designs to protect IP while generating visually realistic synthetic data from a small set of initial examples: a StyleGAN learns the distribution of hardware layout masks to produce novel, macroscopically varied structures, and a conditional GAN (Pix2PixHD) translates these masks into realistic SEM images preserving authentic textures and noise. The key finding is that a segmentation model trained solely on this synthetic data achieves successful sim-to-real transfer to real images and outperforms baselines trained on limited real data. Because the synthetic layouts are novel and do not replicate proprietary routing, deploying the segmentation model reduces exposure to gradient inversion and membership inference attacks, offering a secure, high-performance solution for hardware assurance.

Paper Overview

Field: Computer Vision Authors: Gijung Lee, Ronald Wilson, Damon L. Woodard Published: 2026-08-11 arXiv: 2508.03801

Problem

Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but assembling the large, high-quality datasets required for automated analysis is impeded by:

Time-intensive image acquisition
Strict intellectual property (IP) constraints on proprietary designs

Proposed Approach

The authors introduce a privacy-preserving pipeline that secures IP by heavily distorting the functional design while generating a visually realistic synthetic dataset from a small set of initial examples:

1. StyleGAN learns the distribution of hardware layout masks to generate novel, macroscopically varied structures. 2. Pix2PixHD (a conditional GAN) translates these masks into realistic SEM images that preserve authentic textures and noise.

Key Findings

A segmentation model trained only on synthetic data achieves successful sim-to-real transfer to real images.
It outperforms baselines trained on limited real datasets.
Because the underlying synthetic layouts are clearly novel and do not replicate any specific proprietary routing of the original designs, deploying the final segmentation model mitigates the risk of exposing sensitive IP to attacks such as gradient inversion and membership inference.

Conclusion

The pipeline provides a highly secure, high-performance solution for hardware assurance, addressing both data scarcity and confidentiality requirements.

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Original abstract: Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but assembling the large, high-quality datasets required for automated analysis is impeded by time-intensive acquisition and strict intellectual property (IP) constraints on proprietary designs. We propose a privacy-preserving pipeline that secures IP by heavily distorting the functional design while generating a visually realistic synthetic dataset from a small set of initial examples. A StyleGAN first learns the distribution of hardware layout masks to generate novel, macroscopically varied structures. Subsequently, a conditional GAN (Pix2PixHD) translates these masks into realistic SEM images that preserve authentic textures and noise.

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