Regularized Ensembles and Transferability in Adversarial Learning

2018/12/05 by Yifan Chen, Chen, Yifan, Yevgeniy Vorobeychik +1
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1812.01821

openalex publication_date 2018/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Despite the considerable success of convolutional neural networks in a broad array of domains, recent research has shown these to be vulnerable to small adversarial perturbations, commonly known as adversarial examples. Moreover, such examples have shown to be remarkably portable, or transferable, from one model to another, enabling highly successful black-box attacks. We explore this issue of transferability and robustness from two dimensions: first, considering the impact of conventional l_p regularization as well as replacing the top layer with a linear support vector machine (SVM), and second, the value of combining regularized models into an ensemble. We show that models trained with different regularizers present barriers to transferability, as does partial information about the models comprising the ensemble.

Citations

• Intriguing properties of neural networks
• Parseval Networks: Improving Robustness to Adversarial Examples
• Deep Residual Learning for Image Recognition
• DeepFool: a simple and accurate method to fool deep neural networks
• Learning Multiple Layers of Features from Tiny Images

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