Results 41 to 50 of about 447,373 (304)
Distributionally Adversarial Attack
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution p(x), typically ...
Tianhang Zheng +2 more
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Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives
, 2022 Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of Things (IoT) applications and services, and wireless communications.
Jadidi, Z, Pal, S, Selvakkumar, A
core +1 more source
Boosting 3D Adversarial Attacks With Attacking on Frequency
IEEE Access, 2022 Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks in the image domain. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest.
Binbin Liu, Jinlai Zhang, Jihong Zhu
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Perceptually Constrained Adversarial Attacks
CoRR, 2021 Motivated by previous observations that the usually applied $L_p$ norms ($p=1,2,\infty$) do not capture the perceptual quality of adversarial examples in image classification, we propose to replace these norms with the structural similarity index (SSIM) measure, which was developed originally to measure the perceptual similarity of images.
Muhammad Zaid Hameed +1 more
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Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
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Adversarial Ranking Attack and Defense [PDF]
, 2020 Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou +4 more
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Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification [PDF]
, 2023 Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks.
Panagiotis Andriotis +8 more
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Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks
IEEE AccessDeep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise ...
Bader Rasheed +4 more
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Aliasing is a Driver of Adversarial Attacks
CoRR, 2022 Aliasing is a highly important concept in signal processing, as careful consideration of resolution changes is essential in ensuring transmission and processing quality of audio, image, and video. Despite this, up until recently aliasing has received very little consideration in Deep Learning, with all common architectures carelessly sub-sampling ...
Adrián Rodríguez-Muñoz +1 more
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Adversarial Attacks on Supervised Energy-Based Anomaly Detection in Clean Water Systems [PDF]
Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity.
Maglaras, Leandros +7 more
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