A data-free membership inference attack reconstructs images from federated learning updates using standard cell library layouts as priors, allowing inference of hardware characteristics such as circuit layers and technology nodes from reconstruction fidelity.
REFICS: As- similating data-driven paradigms into reverse engineering and hardware assurance on integrated circuits,
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Federated learning improves segmentation accuracy for hardware reverse engineering but remains vulnerable to recovering proprietary SEM images via gradient inversion attacks.
DECIFR shows that public standard cell library layouts enable a no-auxiliary-data membership inference attack on federated gradient updates by correlating reconstruction quality with training membership in integrated circuit datasets.
citing papers explorer
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A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
A data-free membership inference attack reconstructs images from federated learning updates using standard cell library layouts as priors, allowing inference of hardware characteristics such as circuit layers and technology nodes from reconstruction fidelity.
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Potentials and Pitfalls of Applying Federated Learning in Hardware Assurance
Federated learning improves segmentation accuracy for hardware reverse engineering but remains vulnerable to recovering proprietary SEM images via gradient inversion attacks.
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DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction
DECIFR shows that public standard cell library layouts enable a no-auxiliary-data membership inference attack on federated gradient updates by correlating reconstruction quality with training membership in integrated circuit datasets.