Results 1 to 10 of about 5,389,393 (319)
Survey of Image Adversarial Example Defense Techniques [PDF]
Jisuanji kexue yu tansuo, 2023 The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots.
LIU Ruiqi, LI Hu, WANG Dongxia, ZHAO Chongyang, LI Boyu
doaj +2 more sources
Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications.
Bo Luo, Yannan Liu, Lingxiao Wei, Q. Xu
semanticscholar +2 more sources
A Robust Adversarial Example Attack Based on Video Augmentation
Applied Sciences, 2023 Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin +3 more
doaj +2 more sources
Are adversarial examples inevitable?
CoRR, 2018 ISBN:978-1-7138-7273 ...
Shafahi, Ali +4 more
openaire +4 more sources
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 +5 more
openaire +2 more sources
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
Robust Audio Adversarial Example for a Physical Attack [PDF]
International Joint Conference on Artificial Intelligence, 2018 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 ...
Hiromu Yakura, Jun Sakuma
semanticscholar +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
Optimized Adversarial Example With Classification Score Pattern Vulnerability Removed
IEEE Access, 2022 Neural networks provide excellent service on recognition tasks such as image recognition and speech recognition as well as for pattern analysis and other tasks in fields related to artificial intelligence.
Hyun Kwon, Kyoungmin Ko, Sunghwan Kim
doaj +1 more source
Frontiers in Energy Research, 2023 The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal strategies for them to obtain optimal strategies. Non-etheless,
Donghe Li +5 more
doaj +1 more source