2 research outputs found
Susceptibility of Continual Learning Against Adversarial Attacks
Recent continual learning approaches have primarily focused on mitigating
catastrophic forgetting. Nevertheless, two critical areas have remained
relatively unexplored: 1) evaluating the robustness of proposed methods and 2)
ensuring the security of learned tasks. This paper investigates the
susceptibility of continually learned tasks, including current and previously
acquired tasks, to adversarial attacks. Specifically, we have observed that any
class belonging to any task can be easily targeted and misclassified as the
desired target class of any other task. Such susceptibility or vulnerability of
learned tasks to adversarial attacks raises profound concerns regarding data
integrity and privacy. To assess the robustness of continual learning
approaches, we consider continual learning approaches in all three scenarios,
i.e., task-incremental learning, domain-incremental learning, and
class-incremental learning. In this regard, we explore the robustness of three
regularization-based methods, three replay-based approaches, and one hybrid
technique that combines replay and exemplar approaches. We empirically
demonstrated that in any setting of continual learning, any class, whether
belonging to the current or previously learned tasks, is susceptible to
misclassification. Our observations identify potential limitations of continual
learning approaches against adversarial attacks and highlight that current
continual learning algorithms could not be suitable for deployment in
real-world settings.Comment: 18 pages, 13 figure
Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images
The ongoing COVID-19 pandemic has already taken millions of lives and damaged
economies across the globe. Most COVID-19 deaths and economic losses are
reported from densely crowded cities. It is comprehensible that the effective
control and prevention of epidemic/pandemic infectious diseases is vital.
According to WHO, testing and diagnosis is the best strategy to control
pandemics. Scientists worldwide are attempting to develop various innovative
and cost-efficient methods to speed up the testing process. This paper
comprehensively evaluates the applicability of the recent top ten
state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically
detecting COVID-19 infection using chest X-ray images. Moreover, it provides a
comparative analysis of these models in terms of accuracy. This study
identifies the effective methodologies to control and prevent infectious
respiratory diseases. Our trained models have demonstrated outstanding results
in classifying the COVID-19 infected chest x-rays. In particular, our trained
models MobileNet, EfficentNet, and InceptionV3 achieved a classification
average accuracy of 95\%, 95\%, and 94\% test set for COVID-19 class
classification, respectively. Thus, it can be beneficial for clinical
practitioners and radiologists to speed up the testing, detection, and
follow-up of COVID-19 cases