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Proceedings Paper

Defense against adversarial spectroscopic attacks (Conference Presentation)

Paper Abstract

Raman spectra were perturbed such that an intentional misclassification was induced when using a dimension reduction classifier such as linear discriminate analysis (LDA). These perturbations were primarily targeted at patterning the noise within the spectra such that detection is difficult to detect by visual inspection. Data-intensive decisions are increasingly important to mine the increasing volume of information accessible by modern instrumentation. These decisions are conceptually performed through projection of measurements on high dimensional manifolds to low-dimensional outcomes. This dimension reduction provides suppression of stochastic random noise to better inform the decision. However, non-stochastic patterning of the “noise” can induce intentional misclassification that is difficult to easily detect by visual inspection. Such digital attacks could result in intentional changes in decisions made from many routine automated classifiers. Preliminary results using Raman spectra showed that misclassification can be induced by picking a target classification and patterning the noise in the spectra such that in a reduced dimensional space, it is moved towards the target classification. Development of approaches for optimizing the attacks serves as a prelude for generation of robust classification strategies less susceptible to intentional attacks.

Paper Details

Date Published: 13 May 2019
PDF
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890F (13 May 2019); doi: 10.1117/12.2518922
Show Author Affiliations
Casey Smith, Purdue Univ. (United States)
Youlin Liu, Purdue Univ. (United States)
Garth J. Simpson, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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