An Improved Generation Method of Adversarial Example to Deceive NLP Deep Learning Classifiers

2021 ◽  
pp. 51-62
Author(s):  
Fangzhou Yuan ◽  
Tianyi Zhang ◽  
Xin Liang ◽  
Peihang Li ◽  
Hongzheng Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Classifiers ◽  
Adversarial Example

scholarly journals Analysis of Edge-Optimized Deep Learning Classifiers for Radar-based Gesture Recognition

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mateusz Chmurski ◽  
Mariusz Zubert ◽  
Kay Bierzynski ◽  
Avik Santra
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Learning Classifiers

A semiautomatic annotation approach for sentiment analysis

2021 ◽  
pp. 016555152110065
Author(s):  
Rahma Alahmary ◽  
Hmood Al-Dossari
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Annotation Process ◽  
Learning Classifiers ◽  
Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

scholarly journals Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shize Huang ◽  
Xiaowen Liu ◽  
Xiaolu Yang ◽  
Zhaoxin Zhang ◽  
Lingyu Yang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Gradient Descent ◽  
Learning Networks ◽  
Adversarial Examples ◽  
Environment Perception ◽  
Projected Gradient Descent ◽  
Adversarial Example ◽  
Improved Methods ◽  
Growing Neural Networks

Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety. Only if adversarial attacks are studied thoroughly, researchers can come up with better defence methods against them. However, most existing methods of generating adversarial examples have been devoted to classification, and none of them target tram environment perception systems. In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&W) algorithm to generate adversarial examples against Faster R-CNN object detectors. Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running. We also compare the performance of the two methods, including attack effects, similarity to clean images, and the generating time. The results show that both algorithms can generate adversarial examples within 220 seconds, a much shorter time, without decrease of the success rate.

Generalised rational approximation and its application to improve deep learning classifiers

2021 ◽  
Vol 389 ◽  
pp. 125560 ◽  
Author(s):  
V. Peiris ◽  
N. Sharon ◽  
N. Sukhorukova ◽  
J. Ugon
Keyword(s):  
Deep Learning ◽  
Rational Approximation ◽  
Learning Classifiers

scholarly journals Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

2020 ◽  
Vol 8 ◽  
Author(s):  
Adil Khadidos ◽  
Alaa O. Khadidos ◽  
Srihari Kannan ◽  
Yuvaraj Natarajan ◽  
Sachi Nandan Mohanty ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Data ◽  
Training Data ◽  
Learning Framework ◽  
Learning Classifiers ◽  
Tomography Image ◽  
Mining Model ◽  
Deep Learning Model

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

scholarly journals Deep learning classifiers for hyperspectral imaging: A review

2019 ◽  
Vol 158 ◽  
pp. 279-317 ◽  
Author(s):  
M.E. Paoletti ◽  
J.M. Haut ◽  
J. Plaza ◽  
A. Plaza
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Learning Classifiers
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