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arXiv · 2007.08273

Deep Learning Backdoors

Shaofeng Li·Shiqing Ma·Minhui Xue·Benjamin Zi Hao Zhao
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Abstract

Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to inject hidden malicious behaviors into DNNs such that the backdoor model behaves legitimately for benign inputs, yet invokes a predefined malicious behavior when its input contains a malicious trigger. The trigger can take a plethora of forms, including a special object present in the image (e.g., a yellow pad), a shape filled with custom textures (e.g., logos with particular colors) or even image-wide stylizations with special filters (e.g., images altered by Nashville or Gotham filters). These filters can be applied to the original image by replacing or perturbing a set of image pixels.

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Shaofeng Li, Shiqing Ma, Minhui Xue, Benjamin Zi Hao Zhao. 2020-07-16. Deep Learning Backdoors. https://arxiv.org/abs/2007.08273

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