arXiv subjects

David Huggins

Publications and source records attributed to David Huggins.

2 recordsLinked to original sources

arXiv2023

On the Detection of Shared Data Manipulation in Distributed Optimization

This paper investigates the vulnerability of the Alternating Direction Method of Multipliers (ADMM) algorithm to shared data manipulation, with a focus on solving optimal power flow (OPF) problems. Deliberate data manipulation may cause the ADMM algorithm to converge to suboptimal solutions. We derive a sufficient condition for detecting data manipulation based on the theoretical convergence trajectory of the ADMM algorithm. We evaluate the performance of the detection condition on three data manipulation strategies with various levels of complexity and stealth. The simplest attack sends the target values and each iteration, the second attack uses a feedback loop to find the next target values, and the last attack uses a bilevel optimization to find the target values. We then extend the three data manipulation strategies to avoid detection by the detection conditions and a neural network (NN) detection model. We also propose an adversarial NN training framework to detect shared data manipulation. We illustrate the performance of our data manipulation strategy and detection framework on OPF problems. The results show that the proposed detection condition successfully detects most of the data manipulation attacks. However, the bilevel optimization attack strategy that incorporates the detection methods may avoid being detected. Countering this, our proposed adversarial training framework detects all the instances of the bilevel optimization attack.

eess.SY↗

arXiv2018

Data-Efficient Graph Embedding Learning for PCB Component Detection

This paper presents a challenging computer vision task, namely the detection of generic components on a PCB, and a novel set of deep-learning methods that are able to jointly leverage the appearance of individual components and the propagation of information across the structure of the board to accurately detect and identify various types of components on a PCB. Due to the expense of manual data labeling, a highly unbalanced distribution of component types, and significant domain shift across boards, most earlier attempts based on traditional image processing techniques fail to generalize well to PCB images with various quality, lighting conditions, etc. Newer object detection pipelines such as Faster R-CNN, on the other hand, require a large amount of labeled data, do not deal with domain shift, and do not leverage structure. To address these issues, we propose a three stage pipeline in which a class-agnostic region proposal network is followed by a low-shot similarity prediction classifier. In order to exploit the data dependency within a PCB, we design a novel Graph Network block to refine the component features conditioned on each PCB. To the best of our knowledge, this is one of the earliest attempts to train a deep learning based model for such tasks, and we demonstrate improvements over recent graph networks for this task. We also provide in-depth analysis and discussion for this challenging task, pointing to future research.

cs.CV↗