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

2025-06-06

AI Summary

To address the challenge of attribution—beyond mere detection—of malicious modifications in PCB supply chains, this paper proposes a forensics methodology integrating two-dimensional impedance spectroscopy with eXplainable Artificial Intelligence (XAI). For the first time, SHAP (SHapley Additive exPlanations) is coupled with Random Forest to perform attribution analysis on power delivery network (PDN) frequency-domain impedance signatures, enabling fine-grained localization—from “whether modified” to “where modified” and “which component altered.” The method combines multi-scenario physical tampering modeling with frequency-domain impedance characterization, achieving 96.7% detection accuracy while quantifying each frequency bin’s contribution to classification decisions, thereby supporting reverse-engineering–level forensic traceability. Key innovations include: (i) construction of a two-dimensional impedance feature space tailored for PCB tampering attribution; and (ii) development of the first XAI-enabled impedance signature attribution framework, balancing high accuracy with physics-based interpretability and traceability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

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.

Problem

• Detect PCB tampering using impedance signatures and AI
• Identify modified components in PCBs with explainable AI
• Analyze 2D impedance signatures for forensic tamper detection

Innovation

• Explainable AI for PCB tamper forensic analysis
• Impedance signatures with random forest classifier
• SHAP values to explain classifier decisions