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Vibrational Spectroscopy Can Be Vulnerable to Adversarial Attacks.

Jinchao Liu1,2, Margarita Osadchy3, Yan Wang4

  • 1Institute of Robotics and Automatic Information System (IRAIS), College of Artificial Intelligence, Nankai University, Tianjin 300071, China.

Analytical Chemistry
|October 11, 2024
PubMed
Summary
This summary is machine-generated.

Adversarial attacks using synthetic peaks can fool machine learning models in vibrational spectroscopy. These subtle SynPat attacks are hard for humans to detect, posing security risks to critical applications.

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Area of Science:

  • Spectroscopy
  • Machine Learning
  • Chemical Analysis

Background:

  • Nondestructive vibrational spectroscopy is crucial in fields like pharmacy and security.
  • Machine learning models analyze spectroscopic data, making them targets for adversarial attacks.

Purpose of the Study:

  • To introduce a novel adversarial attack method, SynPat, for vibrational spectroscopy.
  • To assess the vulnerability of machine learning models in this domain to subtle perturbations.

Main Methods:

  • Developed SynPat, an attack generating synthetic peaks via a physical model.
  • Conducted imperceptibility tests with human experts.
  • Evaluated defense mechanisms using an AI detector.

Main Results:

  • SynPat successfully deceived machine learning models for Raman and infrared spectrum analysis.
  • Perturbations were nearly imperceptible to human operators.
  • Conventional and deep learning models demonstrated vulnerability to adversarial attacks.

Conclusions:

  • Machine learning models in vibrational spectroscopy are susceptible to adversarial perturbations.
  • SynPat represents a significant security threat to applications relying on these techniques.
  • This study is the first comprehensive investigation into adversarial robustness in vibrational spectroscopy.