Results 41 to 50 of about 172,371 (266)
IEEE AccessDeep 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 +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
Downstream-agnostic Adversarial Examples
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 This paper has been accepted by the International Conference on Computer Vision (ICCV '23, October 2--6, 2023, Paris, France)
Zhou, Ziqi +6 more
openaire +2 more sources
Distinguishability of adversarial examples [PDF]
Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020 Machine learning models can be easily fooled by adversarial examples which are generated from clean examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection. From a unique angle, we propose
Yi Qin, Ryan Hunt, Chuan Yue
openaire +1 more source
A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
doaj +1 more source
Robust Audio Adversarial Example for a Physical Attack
, 2019 We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not ...
Sakuma, Jun, Yakura, Hiromu
core +1 more source
Simple Transparent Adversarial Examples
, 2021 There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different ...
Borkar, Jaydeep, Chen, Pin-Yu
openaire +2 more sources
Adversarial Training for Free!
, 2019 Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Davis, Larry S. +8 more
core +1 more source
Unrestricted Adversarial Examples
, 2018 We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries.
Brown, Tom B. +5 more
openaire +2 more sources
Exploring Adversarial Examples [PDF]
, 2018 Failure cases of black-box deep learning, e.g. adversarial examples, might have severe consequences in healthcare. Yet such failures are mostly studied in the context of real-world images with calibrated attacks. To demystify the adversarial examples, rigorous studies need to be designed.
Kügler, David +3 more
openaire +2 more sources