scholarly journals Adversarial example detection for DNN models: a review and experimental comparison

2022 ◽  
Author(s):  
Ahmed Aldahdooh ◽  
Wassim Hamidouche ◽  
Sid Ahmed Fezza ◽  
Olivier Déforges
Keyword(s):  
Experimental Comparison ◽  
Adversarial Example

scholarly journals Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13867-13868
Author(s):  
Xiao Liu ◽  
Jing Zhao ◽  
Shiliang Sun
Keyword(s):  
Gradient Descent ◽  
Random Variable ◽  
Misclassification Rate ◽  
Experimental Comparison ◽  
Graph Node ◽  
Adversarial Examples ◽  
Adversarial Attack ◽  
Projected Gradient Descent ◽  
Adversarial Example ◽  
Graph Neural Networks

Adversarial attack on graph neural network (GNN) is distinctive as it often jointly trains the available nodes to generate a graph as an adversarial example. Existing attacking approaches usually consider the case that all the training set is available which may be impractical. In this paper, we propose a novel Bayesian adversarial attack approach based on projected gradient descent optimization, called Bayesian PGD attack, which gets more general attack examples than deterministic attack approaches. The generated adversarial examples by our approach using the same partial dataset as deterministic attack approaches would make the GNN have higher misclassification rate on graph node classification. Specifically, in our approach, the edge perturbation Z is used for generating adversarial examples, which is viewed as a random variable with scale constraint, and the optimization target of the edge perturbation is to maximize the KL divergence between its true posterior distribution p(Z|D) and its approximate variational distribution qθ(Z). We experimentally find that the attack performance will decrease with the reduction of available nodes, and the effect of attack using different nodes varies greatly especially when the number of nodes is small. Through experimental comparison with the state-of-the-art attack approaches on GNNs, our approach is demonstrated to have better and robust attack performance.

How to Measure Discount Rates? An experimental comparison of three methods

2010 ◽  
Author(s):  
David J. Hardisty ◽  
Katherine J. Thompson ◽  
David H. Krantz ◽  
Elke U. Weber
Keyword(s):  
Experimental Comparison ◽  
Discount Rates

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

EXPERIMENTAL COMPARISON OF LOOP HEAT PIPE PERFORMANCE WITH VARIOUS EVAPORATOR DESIGNS

2014 ◽  
Vol 5 (1-4) ◽  
pp. 287-294
Author(s):  
S. Ouenzerfi ◽  
T. Barreteau ◽  
C. Petit ◽  
Valerie Sartre ◽  
Jocelyn Bonjour ◽  
...  
Keyword(s):  
Heat Pipe ◽  
Experimental Comparison ◽  
Loop Heat Pipe
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