Adversarial example - Open Access .click

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Using genomic context informed genotype data and within-model ancestry adjustment to classify type 2 diabetes. [PDF]

Commun Med (Lond)
Barnett EJ +4 more
europepmc +1 more source

Constrained graph dynamic spatial perception adversarial network for human motion generation. [PDF]

PLoS One
Li W +6 more
europepmc +1 more source

Robust Deep Active Learning via Distance-Measured Data Mixing and Adversarial Training. [PDF]

Entropy (Basel)
Song S, Wang X, Dong S, Jiang J.
europepmc +1 more source

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deep learning
adversarial attack
evasion attack

machine learning
deep neural network
adversarial defense

adversarial example detection
deep neural network dnn
image classification

Principal Component Adversarial Example

IEEE Transactions on Image Processing, 2020
Despite having achieved excellent performance on various tasks, deep neural networks have been shown to be susceptible to adversarial examples, i.e., visual inputs crafted with structural imperceptible noise. To explain this phenomenon, previous works implicate the weak capability of the classification models and the difficulty of the classification ...
Yonggang Zhang +4 more
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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, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li +4 more
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Adversarial Examples in Arabic

2019 International Conference on Computational Science and Computational Intelligence (CSCI), 2019
Several studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples - perturbed inputs that cause DNN-based models to produce incorrect outputs. A variety of adversarial attacks have been proposed in the domains of computer vision and natural language processing (NLP); however, most attacks in the NLP domain have been ...
Basemah Alshemali, Jugal Kalita
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Revealing Perceptual Proxies with Adversarial Examples

IEEE Transactions on Visualization and Computer Graphics, 2021
Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, as an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks,
Brian D, Ondov +4 more
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Adversarial Examples for Hamming Space Search

IEEE Transactions on Cybernetics, 2020
Due to its strong representation learning ability and its facilitation of joint learning for representation and hash codes, deep learning-to-hash has achieved promising results and is becoming increasingly popular for the large-scale approximate nearest neighbor search.
Erkun Yang +3 more
openaire +2 more sources

deep learning
adversarial attack
evasion attack

machine learning
deep neural network
adversarial defense

adversarial example detection
deep neural network dnn
image classification