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Adversarial example defense based on image reconstruction [PDF]
PeerJ Computer Science, 2021 The rapid development of deep neural networks (DNN) has promoted the widespread application of image recognition, natural language processing, and autonomous driving.
Yu(AUST) Zhang +3 more
doaj +3 more sources
Targeted Speech Adversarial Example Generation With Generative Adversarial Network
IEEE Access, 2020 Although neural network-based speech recognition models have enjoyed significant success in many acoustic systems, they are susceptible to be attacked by the adversarial examples.
Donghua Wang +4 more
doaj +2 more sources
Learning Universal Adversarial Perturbation by Adversarial Example
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level ...
Maosen Li +4 more
semanticscholar +2 more sources
Weighted-Sampling Audio Adversarial Example Attack [PDF]
AAAI Conference on Artificial Intelligence, 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
core +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.
Liu, Yannan +3 more
core +3 more sources
Robust Audio Adversarial Example for a Physical Attack [PDF]
International Joint Conference on Artificial Intelligence, 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 +2 more sources
Applied Sciences, 2020 State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks.
Sanglee Park, Jungmin So
doaj +2 more sources
Smooth adversarial examples [PDF]
EURASIP Journal on Information Security, 2020 This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts.
Hanwei Zhang +3 more
doaj +4 more sources
Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences [PDF]
SensorsThe threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection.
Hua Mu +4 more
doaj +2 more sources
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
doaj +2 more sources