Maryam Rastgarpour

Amir Hossein Khorasani, Ali Jahanian, Maryam Rastgarpour

Machine learning is a powerful tool for building predictive models. However, it is vulnerable to adversarial attacks. Fast Gradient Sign Method (FGSM) attacks are a common type of adversarial attack that adds small perturbations to input data to trick a model into misclassifying it. In response to these attacks, researchers have developed methods for "unlearning" these attacks, which involves retraining a model on the original data without the added perturbations. Machine unlearning is a technique that tries to "forget" specific data points from the training dataset, to improve the robustness of a machine learning model against adversarial attacks like FGSM. In this paper, we focus on applying unlearning techniques to the LeNet neural network, a popular architecture for image classification. We evaluate the efficacy of unlearning FGSM attacks on the LeNet network and find that it can significantly improve its robustness against these types of attacks.

Mozhgan Naderi, Maryam Rastgarpour, Amir Reza Takhsha

Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this study, we proposed two low-parameter Convolutional Neural Networks (CNNs), IR-BRAINNET and Modified-DEMNET, designed to detect the early stages of AD accurately. We also introduced an ensemble model that averages their outputs to reduce variance across the CNNs and enhance AD detection. Both CNNs are trained, and all models are evaluated using a Magnetic Resonance Imaging (MRI) dataset from the Kaggle database. The dataset includes images of four stages of dementia, with an uneven class distribution. To mitigate challenges stemming from the inherent imbalance in the dataset, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to generate additional instances for minority classes. In the NO-SMOTE scenario, despite the imbalanced distribution, the ensemble model achieved 98.28% accuracy, outperforming IR-BRAINNET (97.26%) and Modified-DEMNET (95.54%), with Wilcoxon p-values of 2.9e-3 and 5.20e-6, respectively, indicating significant improvement in correct predictions through the use of the average function. In the SMOTE scenario, the ensemble model achieved 99.92% accuracy (1.64% improvement over NO-SMOTE), IR-BRAINNET reached 99.80% (2.54% improvement), and Modified-DEMNET attained 99.72% (4.18% improvement). Based on the experimental findings, averaging the models' outputs enhanced AD diagnosis in both scenarios, while the diversity in the dataset introduced by SMOTE-generated instances significantly improved performance. Furthermore, the compact models we proposed outperformed those from previous studies, even in the presence of an imbalanced distribution.