0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
A large input-space-margin approach for adversarial training
Authors :
Reihaneh Nikouei
1
Mohammad Taheri
2
1- Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran.
2- Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran.
Keywords :
Adversarial attack،Large margin،Input space،Defense method
Abstract :
It is shown that machine learning models are vulnerable to adversarial attacks. Therefore, different defense methods such as adversarial training have been proposed to improve models’ robustness against these attacks. Some recent approaches proposed to structurally improve the robustness of the models. For example, large margin methods try to increase a margin, empty of instances, along decision boundaries that structurally increase necessary change to modify a training instance to an adversarial one. However, nonlinear large-margin models, maximize the margin in a high dimensional space although adversarial examples are generated with a little change in the original space. In this paper, a novel mixed approach is proposed, called LIM (Large Input Margin) to improve the robustness of the model by minimizing both structural and empirical risks. Specifically, both training and adversarial example generation are done based on a loss function to maximize the margin in the original feature space even in a non-linear model. The proposed method is evaluated with FGSM and PGD attacks on MNIST and CIFAR10 datasets. The experimental results show that LIM method outperforms the state-of-the-art defense methods significantly and improves adversarial robustness against FGSM and PGD attacks on both datasets.
Papers List
List of archived papers
FAST: FPGA Acceleration of Neural Networks Training
Alireza Borhani - Mohammad Hossein Goharinejad - Hamid Reza Zarandi
Optimizing Foreign Exchange Trading Performance Through Reinforcement Machine Learning Framework
Ervin Gubin Moung - Hani Yasmin Binti Murnizam - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Lorita Angeline
Real-Time Vehicle Detection and Classification in UAV imagery Using Improved YOLOv5
Mohammad Hossein Hamzenejadi - Hadis Mohseni
A Comprehensive Dataset of Real-scene Images for Text Detection and Recognition in Persian
Iman Souzanchi - Ramin Rahimi - Mohammad Ali Majidi Anvari - Atefeh Baniasadi - Ashkan Sadeghi - Mohammad Reza Mohammadi
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
Aref Farhadipour - Pouya Taghipour
Automated Person Identification from Hand Images\\using Hierarchical Vision Transformer Network
Zahra Ebrahimian - Seyed Ali Mirsharji - Ramin Toosi - Mohammad Ali Akhaee
Soccer Video Event Detection Using Metric Learning
Ali Karimi - Ramin Toosi - Mohammad Ali Akhaee
Explainable Error Detection Method for Structured Data using HoloDetect framework
Abolfazl Mohajeri Khorasani - Sahar Ghassabi - Behshid Behkamal - Mostafa Milani
Parallel Local Feature Selection For High-dimensional Data
Zhaleh Manbari - Chiman Salavati - Fardin AkhlaghianTab - Barzan Saeedpoor - Himan Delbina - Mahmud Abdulla Mohammad
An Overview of Regression Methods in Early Prediction of Movie Ratings
Houmaan Chamani - Zhivar Sourati Hassanzadeh - Behnam Bahrak
more
Samin Hamayesh - Version 41.7.6