Robustness of Generalized Learning Vector Quantization Models against Adversarial AttacksRobustness of Generalized Learning Vector Quantization Models against Adversarial AttacksSascha Saralajew and Lars Holdijk and Maike Rees and Thomas Villmann2019
Paper summarydavidstutzSaralajew et al. evaluate learning vector quantization (LVQ) approaches regarding their robustness against adversarial examples. In particular, they consider generalized LVQ where examples are classified based on their distance to the closest prototype of the same class and the closest prototype of another class. The prototypes are learned during training; I refer to the paper for details. Robustness is compared to adversarial training and evaluated against several attacks, including FGSM, DeepFool and Boundary – both white-box and black-box attacks. Regarding $L_\infty$, LVQ usually demonstrates poorer performance than adversarial training. Still, robustness seems to be higher than normally trained deep neural networks. One of the main explanations of the authors is that LVQ follows a max-margin approach; this max-margin idea seems to favor robust models.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
First published: 2019/02/01 (1 year ago) Abstract: Adversarial attacks and the development of (deep) neural networks robust
against them are currently two widely researched topics. The robustness of
Learning Vector Quantization (LVQ) models against adversarial attacks has
however not yet been studied to the same extent. We therefore present an
extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix
LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized
LVQ and Generalized Tangent LVQ have a high base robustness, on par with the
current state-of-the-art in robust neural network methods. In contrast to this,
Generalized Matrix LVQ shows a high susceptibility to adversarial attacks,
scoring consistently behind all other models. Additionally, our numerical
evaluation indicates that increasing the number of prototypes per class
improves the robustness of the models.
Saralajew et al. evaluate learning vector quantization (LVQ) approaches regarding their robustness against adversarial examples. In particular, they consider generalized LVQ where examples are classified based on their distance to the closest prototype of the same class and the closest prototype of another class. The prototypes are learned during training; I refer to the paper for details. Robustness is compared to adversarial training and evaluated against several attacks, including FGSM, DeepFool and Boundary – both white-box and black-box attacks. Regarding $L_\infty$, LVQ usually demonstrates poorer performance than adversarial training. Still, robustness seems to be higher than normally trained deep neural networks. One of the main explanations of the authors is that LVQ follows a max-margin approach; this max-margin idea seems to favor robust models.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).