8 accepted papers
This paper considers the problem of performing post-attack forensic analysis for a test-time attack on a machine learning model. A test-time attack can be represented as a mapping that receives a benign test example as the input and outputs a falsified version of it. Given a set of attacked examples…
Transmission line parameters play a key role when accurately modeling the power network. However, several factors such as environmental conditions, errors in manufacturing data, and uninformed network changes can introduce errors to the line parameter information at the control center. In this paper…
We consider the problem of designing a robust classifier in the presence of an adversary who aims to degrade classification performance by elaborately falsifying the test instance. We propose a model-agnostic defense approach wherein the true class label of the falsified instance is inferred by anal…
The problem of making the classifier design resilient to test data falsification is considered. In the literature, a few countermeasures have been proposed to defend machine learning algorithms against test data falsification, but a common assumption employed therein is that feature entries of test…
The hyperspectral unmixing problem arises in remote sensing, chemometrics, and biomedical engineering applications. The spectral signature of a single pixel in a hyperspectral cube can be represented as a non-negative combination of non-negative signatures from various materials contained in the phy…
The problem of correcting power network parameters and topology using multi-period SCADA measurements is considered. Starting from the current knowledge of parameter values, we formulate the parameter correction problem as a sparse unsupervised regression problem by exploiting the sparsity of the pa…
Recognizing and localizing a recurring pattern is a problem with a variety of applications such as classification and localization of home appliances from their activation signals and estimating the relative alignment between records of a natural repetitive electrocardiography (ECG) signals in Bio-m…
We study sparse gross error correction for state estimation in a non-linear sensing system. We consider a practical assumption that gross errors are sparse, and their locations tend to be invariant over a few consecutive measurement periods. Under the assumption, a robust state estimation and error…