Geometric robustness of deep networks: analysis and improvement Geometric robustness of deep networks: analysis and improvement
Paper summary Kanbak et al. propose ManiFool, a method to determine a network’s invariance to transformations by iteratively finding adversarial transformations. In particular, given a class of transformations to consider, ManiFool iteratively alternates two steps. First, a gradient step is taken in order to move into an adversarial direction; then, the obtained perturbation/direction is projected back to the space of allowed transformations. While the details are slightly more involved, I found that this approach is similar to the general projected gradient ascent approach to finding adversarial examples. By finding worst-case transformations for a set of test samples, Kanbak et al. Are able to quantify the invariance of a network against specific transformations. Furthermore, they show that adversarial fine-tuning using the found adversarial transformations allows to boost invariance, while only incurring a small loss in general accuracy. Examples of the found adversarial transformations are shown in Figure 1. https://i.imgur.com/h83RdE8.png Figure 1: The proposed attack method allows to consider different classes of transformations as shown in these examples. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
arxiv.org
scholar.google.com
Geometric robustness of deep networks: analysis and improvement
Kanbak, Can and Moosavi-Dezfooli, Seyed-Mohsen and Frossard, Pascal
arXiv e-Print archive - 2017 via Local Bibsonomy
Keywords: dblp


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Summary by David Stutz 9 months ago
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