scholarly journals Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum

2021 ◽  
Author(s):  
Tiangang Li
Keyword(s):  
Deep Learning ◽  
Success Rate ◽  
Learning Algorithm ◽  
Training Model ◽  
Great Success ◽  
Deep Learning Algorithm ◽  
Gradient Descent Algorithm ◽  
Adversarial Examples ◽  
Projected Gradient Descent ◽  
Public Datasets

The deep learning algorithm has achieved great success in the field of computer vision, but some studies have pointed out that the deep learning model is vulnerable to attacks adversarial examples and makes false decisions. This challenges the further development of deep learning, and urges researchers to pay more attention to the relationship between adversarial examples attacks and deep learning security. This work focuses on adversarial examples, optimizes the generation of adversarial examples from the view of adversarial robustness, takes the perturbations added in adversarial examples as the optimization parameter. We propose RWR-NM-PGD attack algorithm based on random warm restart mechanism and improved Nesterov momentum from the view of gradient optimization. The algorithm introduces improved Nesterov momentum, using its characteristics of accelerating convergence and improving gradient update direction in optimization algorithm to accelerate the generation of adversarial examples. In addition, the random warm restart mechanism is used for optimization, and the projected gradient descent algorithm is used to limit the range of the generated perturbations in each warm restart, which can obtain better attack effect. Experiments on two public datasets show that the algorithm proposed in this work can improve the success rate of attacking deep learning models without extra time cost. Compared with the benchmark attack method, the algorithm proposed in this work can achieve better attack success rate for both normal training model and defense model. Our method has average attack success rate of 46.3077%, which is 27.19% higher than I-FGSM and 9.27% higher than PGD. The attack results in 13 defense models show that the attack algorithm proposed in this work is superior to the benchmark algorithm in attack universality and transferability.

scholarly journals Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum

2021 ◽  
Author(s):  
Tiangang Li
Keyword(s):  
Deep Learning ◽  
Success Rate ◽  
Learning Algorithm ◽  
Training Model ◽  
Great Success ◽  
Deep Learning Algorithm ◽  
Gradient Descent Algorithm ◽  
Adversarial Examples ◽  
Projected Gradient Descent ◽  
Public Datasets

The deep learning algorithm has achieved great success in the field of computer vision, but some studies have pointed out that the deep learning model is vulnerable to attacks adversarial examples and makes false decisions. This challenges the further development of deep learning, and urges researchers to pay more attention to the relationship between adversarial examples attacks and deep learning security. This work focuses on adversarial examples, optimizes the generation of adversarial examples from the view of adversarial robustness, takes the perturbations added in adversarial examples as the optimization parameter. We propose RWR-NM-PGD attack algorithm based on random warm restart mechanism and improved Nesterov momentum from the view of gradient optimization. The algorithm introduces improved Nesterov momentum, using its characteristics of accelerating convergence and improving gradient update direction in optimization algorithm to accelerate the generation of adversarial examples. In addition, the random warm restart mechanism is used for optimization, and the projected gradient descent algorithm is used to limit the range of the generated perturbations in each warm restart, which can obtain better attack effect. Experiments on two public datasets show that the algorithm proposed in this work can improve the success rate of attacking deep learning models without extra time cost. Compared with the benchmark attack method, the algorithm proposed in this work can achieve better attack success rate for both normal training model and defense model. Our method has average attack success rate of 46.3077%, which is 27.19% higher than I-FGSM and 9.27% higher than PGD. The attack results in 13 defense models show that the attack algorithm proposed in this work is superior to the benchmark algorithm in attack universality and transferability.

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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