Results 41 to 50 of about 177,286 (215)

Boundary Adversarial Examples Against Adversarial Overfitting

CoRR, 2022
Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing ...
Muhammad Zaid Hameed, Beat Buesser
openaire   +2 more sources

An Effective Adversarial Attack on Person Re-Identification in Video Surveillance via Dispersion Reduction

IEEE Access, 2020
Person re-identification across a network of cameras, with disjoint views, has been studied extensively due to its importance in wide-area video surveillance.
Yu Zheng, Yantao Lu, Senem Velipasalar
doaj   +1 more source

Adversarial Examples for Electrocardiograms

CoRR, 2019
In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can be embedded in medical devices and wearable products such as the injectable Medtronic Linq monitor, the iRhythm Ziopatch wearable monitor, and the Apple Watch Series 4 ...
Xintian Han, Yuxuan Hu, Luca Foschini 0002, Larry Chinitz, Lior Jankelson, Rajesh Ranganath   +5 more
openaire   +2 more sources

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

IEEE Access, 2022
The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in ...
Ibrahim Aliyu, Selinde Van Engelenburg, Muhammed Bashir Mu'Azu, Jinsul Kim, Chang Gyoon Lim   +4 more
doaj   +1 more source

On the Geometry of Adversarial Examples

CoRR, 2018
Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of ...
Marc Khoury, Dylan Hadfield-Menell
openaire   +2 more sources

Adversarial Examples in the Physical World [PDF]

, 2018
Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it.
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio   +2 more
openaire   +3 more sources

Human-Producible Adversarial Examples

CoRR, 2023
Submitted to ICLR ...
David Khachaturov, Yue Gao 0011, Ilia Shumailov, Robert D. Mullins, Ross J. Anderson, Kassem Fawaz   +5 more
openaire   +2 more sources

A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach

IEEE Access
Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs).
Mohammed Nasser Al-Andoli, Shing Chiang Tan, Kok Swee Sim, Pey Yun Goh, Chee Peng Lim   +4 more
doaj   +1 more source

Improving Adversarial Robustness of CNNs via Maximum Margin

Applied Sciences, 2022
In recent years, adversarial examples have aroused widespread research interest and raised concerns about the safety of CNNs. We study adversarial machine learning inspired by a support vector machine (SVM), where the decision boundary with maximum ...
Jiaping Wu, Zhaoqiang Xia, Xiaoyi Feng
doaj   +1 more source

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

, 2018
Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs.
Chen, Yiran, Cheng, Hsin-Pai, Li, Hai, Li, Sicheng, Song, Chang, Wu, Chunpeng, Wu, Qing, Yang, Huanrui   +7 more
core   +1 more source