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Hassan Khazane, Mohammed Ridouani, Fatima Salahdine and Naima Kaabouch
With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are dep...
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Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner...
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Sharoug Alzaidy and Hamad Binsalleeh
In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs,...
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Meng Bi, Xianyun Yu, Zhida Jin and Jian Xu
In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP metho...
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William Villegas-Ch, Angel Jaramillo-Alcázar and Sergio Luján-Mora
This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner ...
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Woonghee Lee and Younghoon Kim
This study introduces a deep-learning-based framework for detecting adversarial attacks in CT image segmentation within medical imaging. The proposed methodology includes analyzing features from various layers, particularly focusing on the first layer, a...
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Danilo Pau, Andrea Pisani and Antonio Candelieri
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ...
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Minxiao Wang, Ning Yang, Dulaj H. Gunasinghe and Ning Weng
Utilizing machine learning (ML)-based approaches for network intrusion detection systems (NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to various threats. Of particular concern are two significant threats associate...
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Suliman A. Alsuhibany
The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been a topic of interest for several years. The ability of computers to recognize CAPTCHA has significantly increased due to the development of deep le...
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Saqib Ali, Sana Ashraf, Muhammad Sohaib Yousaf, Shazia Riaz and Guojun Wang
The successful outcomes of deep learning (DL) algorithms in diverse fields have prompted researchers to consider backdoor attacks on DL models to defend them in practical applications. Adversarial examples could deceive a safety-critical system, which co...
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