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Backpropagating Linearly Improves Transferability of Adversarial Examples

Yiwen Guo · Qizhang Li · Hao Chen

Poster Session 2 #681

Keywords: [ Algorithms ] [ AutoML ] [ Algorithms -> Classification; Algorithms -> Online Learning; Applications -> Computer Vision; Deep Learning; Deep Learning ] [ C ]


The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community. In this paper, we study the transferability of such examples, which lays the foundation of many black-box attacks on DNNs. We revisit a not so new but definitely noteworthy hypothesis of Goodfellow et al.'s and disclose that the transferability can be enhanced by improving the linearity of DNNs in an appropriate manner. We introduce linear backpropagation (LinBP), a method that performs backpropagation in a more linear fashion using off-the-shelf attacks that exploit gradients. More specifically, it calculates forward as normal but backpropagates loss as if some nonlinear activations are not encountered in the forward pass. Experimental results demonstrate that this simple yet effective method obviously outperforms current state-of-the-arts in crafting transferable adversarial examples on CIFAR-10 and ImageNet, leading to more effective attacks on a variety of DNNs. Code at:

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