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Really natural adversarial examples [PDF]
International Journal of Machine Learning and Cybernetics, 2021 AbstractThe phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the effect is striking, it has been suggested that such carefully selected injected noise does not necessarily ...
Anibal Pedraza +2 more
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Adversarial Examples for Generative Models [PDF]
2018 IEEE Security and Privacy Workshops (SPW), 2018 We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks.
Kos, Jernej, Fischer, Ian, Song, Dawn
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Boundary Adversarial Examples Against Adversarial Overfitting
, 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 ...
Hameed, Muhammad Zaid, Buesser, Beat
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Adversarial examples in remote sensing [PDF]
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018 This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet. In particular, we present a new study of adversarial examples in the context of satellite image classification
Czaja, Wojciech +4 more
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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
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Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection [PDF]
PeerJ Computer Science, 2023 Deep neural networks (DNNs) are increasingly being used in malware detection and their robustness has been widely discussed. Conventionally, the development of an adversarial example generation scheme for DNNs involves either detailed knowledge ...
Ruijin Sun +6 more
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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 +4 more
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, 2020 Appears in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Bose, Avishek Joey +6 more
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Adversarial Examples Detection Beyond Image Space [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 To appear in ICASSP ...
Chen, Kejiang +6 more
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Are adversarial examples inevitable?
, 2018 ISBN:978-1-7138-7273 ...
Shafahi, Ali +4 more
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