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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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Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
IEEE Access, 2018 Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon +4 more
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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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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
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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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IET Image Processing, 2023 Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment.
Dong Yang, Wei Chen, Songjie Wei
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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
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Weighted-Sampling Audio Adversarial Example Attack
, 2020 Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks ...
Ding, Yufei +4 more
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