There's Waldo: PCB Tamper Forensic Analysis using Explainable AI on Impedance Signatures

• URL: http://arxiv.org/abs/2506.05734v1
• Date: Fri, 06 Jun 2025 04:31:49 GMT
• Title: There's Waldo: PCB Tamper Forensic Analysis using Explainable AI on Impedance Signatures
• Authors: Maryam Saadat Safa, Seyedmohammad Nouraniboosjin, Fatemeh Ganji, Shahin Tajik,
• Abstract summary: Security of printed circuit boards (PCBs) has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems.<n>One non-invasive and reliable PCB tamper detection technique with global coverage is the impedance characterization of a PCB's power delivery network (PDN)<n>In this work, we introduce a novel PCB forensics approach using explainable AI (XAI) on impedance signatures.
• Score: 4.719825216300636
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The security of printed circuit boards (PCBs) has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems. While detecting tampering on a PCB is the first step for verification, forensics is also needed to identify the modified component. One non-invasive and reliable PCB tamper detection technique with global coverage is the impedance characterization of a PCB's power delivery network (PDN). However, it is an open question whether one can use the two-dimensional impedance signatures for forensics purposes. In this work, we introduce a novel PCB forensics approach using explainable AI (XAI) on impedance signatures. Through extensive experiments, we replicate various PCB tamper events, generating a dataset used to develop an XAI algorithm capable of not only detecting tampering but also explaining why the algorithm makes a decision about whether a tamper event has happened. At the core of our XAI algorithm is a random forest classifier with an accuracy of 96.7%, sufficient to explain the algorithm's decisions. To understand the behavior of the classifier in the decision-making process, we utilized SHAP values as an XAI tool to determine which frequency component influences the classifier's decision for a particular class the most. This approach enhances detection capabilities as well as advancing the verifier's ability to reverse-engineer and analyze two-dimensional impedance signatures for forensics.

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