Adversarial Attacks on Deep Neural Networks for Time Series Classification

Fawaz, Hassan Ismail; Forestier, Germain; Weber, Jonathan; Idoumghar, Lhassane; Müller, Pierre-Alain

doi:10.1109/ijcnn.2019.8851936

Cited by 73 publications

(38 citation statements)

References 29 publications

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“…Hence, observing the gaps in the works already done gives a fair idea as to what improvements can be done. [13] III. OBJECTIVES AND PROPOSED METHODOLOGY Adversarial machine learning can be used to implement techniques that are cyber attacks by nature,which can be categorised based on the resources available to the attacker: 1.…”

Section: Literature Surveymentioning

confidence: 99%

Facial Privacy Preservation using FGSM and Universal Perturbation attacks

Nishchal¹

2022

Preprint

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<p>Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual’s face image with high accuracy. Many techniques have been proposed to ensure user privacy, such as visible distortions to the images, manipulation of the original image with new face attributes, face swapping etc. Though these techniques achieve the goal of user privacy by fooling face recognition models, they don’t help the user when they want to upload original images without visible distortions or manipulation. The objective of this work is to implement techniques to ensure the privacy of user’s sensitive or personal data in face images by creating minimum pixel level distortions using white-box and black-box perturbation algorithms to fool AI models while maintaining the integrity of the image, so as to appear the same to a human eye.</p><div><br></div>

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Section: Literature Surveymentioning

confidence: 99%

Facial Privacy Preservation using FGSM and Universal Perturbation attacks

Nishchal¹

2022

Preprint

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“…This highlights that there are only a few works that evaluate the robustness of models by providing a reproducible model of their (natural) perturbations or a (artificially) perturbed dataset that is publicly available. Only 3 studies (i.e., [ 29 , 32 , 40 ]) out of 14 (artificially) generated perturbations to modify the datasets and then perform experiments on the models. Other listed studies do not provide a fault model that is reproducible, or they use datasets that are known as containing some perturbations (noise or missing data) but without identifying the characteristics of these perturbations (making it difficult to reproduce on other datasets for comparison purposes).…”

Section: Background and Related Workmentioning

confidence: 99%

A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings

Benedick

Robert²,

Traon

2021

Sensors

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Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes to solving data-analysis problems. Industry are conducting their digital shifts, and AI is becoming a cornerstone technology for making decisions out of the huge amount of (sensors-based) data available in the production floor. However, such technology may be disappointing when deployed in real conditions. Despite good theoretical performances and high accuracy when trained and tested in isolation, a Machine-Learning (M-L) model may provide degraded performances in real conditions. One reason may be fragility in treating properly unexpected or perturbed data. The objective of the paper is therefore to study the robustness of seven M-L and Deep-Learning (D-L) algorithms, when classifying univariate time-series under perturbations. A systematic approach is proposed for artificially injecting perturbations in the data and for evaluating the robustness of the models. This approach focuses on two perturbations that are likely to happen during data collection. Our experimental study, conducted on twenty sensors’ datasets from the public University of California Riverside (UCR) repository, shows a great disparity of the models’ robustness under data quality degradation. Those results are used to analyse whether the impact of such robustness can be predictable—thanks to decision trees—which would prevent us from testing all perturbations scenarios. Our study shows that building such a predictor is not straightforward and suggests that such a systematic approach needs to be used for evaluating AI models’ robustness.

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“…In addition, sequential data mining tasks such as natural language processing and speech recognition are being tackled with deep convolutional, recurrent and generative adversarial neural networks [31], [32]. Inspired by this recent success of deep learning models, researchers started adopting these complex machine learning techniques to solve the underlying task of Time Series Classification [19], [33]. Specifically Wang et al [18] showed very promising results, where a Fully Convolutional Neural network (FCN) and a Residual Network (ResNet) were designed to reach COTE's performance when evaluated on 44 datasets from the UCR/UEA archive [7], [20].…”

Section: A Neural Network For Time Series Classificationmentioning

confidence: 99%

Deep Neural Network Ensembles for Time Series Classification

Fawaz

Forestier

Weber

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE.

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Adversarial Attacks on Deep Neural Networks for Time Series Classification

Cited by 73 publications

References 29 publications

Facial Privacy Preservation using FGSM and Universal Perturbation attacks

Facial Privacy Preservation using FGSM and Universal Perturbation attacks

A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings

Deep Neural Network Ensembles for Time Series Classification

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