Adversarial Examples on Segmentation Models Can be Easy to Transfer

• URL: http://arxiv.org/abs/2111.11368v1
• Date: Mon, 22 Nov 2021 17:26:21 GMT
• Title: Adversarial Examples on Segmentation Models Can be Easy to Transfer
• Authors: Jindong Gu, Hengshuang Zhao, Volker Tresp, Philip Torr
• Abstract summary: The transferability of adversarial examples on classification models has attracted a growing interest. We study the overfitting phenomenon of adversarial examples on classification and segmentation models. We propose a simple and effective method, dubbed dynamic scaling, to overcome the limitation.
• Score: 21.838878497660353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural network-based image classification can be misled by adversarial examples with small and quasi-imperceptible perturbations. Furthermore, the adversarial examples created on one classification model can also fool another different model. The transferability of the adversarial examples has recently attracted a growing interest since it makes black-box attacks on classification models feasible. As an extension of classification, semantic segmentation has also received much attention towards its adversarial robustness. However, the transferability of adversarial examples on segmentation models has not been systematically studied. In this work, we intensively study this topic. First, we explore the overfitting phenomenon of adversarial examples on classification and segmentation models. In contrast to the observation made on classification models that the transferability is limited by overfitting to the source model, we find that the adversarial examples on segmentations do not always overfit the source models. Even when no overfitting is presented, the transferability of adversarial examples is limited. We attribute the limitation to the architectural traits of segmentation models, i.e., multi-scale object recognition. Then, we propose a simple and effective method, dubbed dynamic scaling, to overcome the limitation. The high transferability achieved by our method shows that, in contrast to the observations in previous work, adversarial examples on a segmentation model can be easy to transfer to other segmentation models. Our analysis and proposals are supported by extensive experiments.

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