A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2008.00047v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset to influence the training process. Prior works focus on either availability attacks (i.e., lowering the overall model accuracy) or integrity attacks (i.e., enabling specific instance-based backdoor). In this paper, we advance the adversarial objectives of the availability attacks to a per-class basis, which we refer to as class-oriented poisoning<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.00047v2">arXiv:2008.00047v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uavhhfqm2bfebi3ph7lkxqli7q">fatcat:uavhhfqm2bfebi3ph7lkxqli7q</a> </span>
more »... cks. We demonstrate that the proposed attack is capable of forcing the corrupted model to predict in two specific ways: (i) classify unseen new images to a targeted "supplanter" class, and (ii) misclassify images from a "victim" class while maintaining the classification accuracy on other non-victim classes. To maximize the adversarial effect as well as reduce the computational complexity of poisoned data generation, we propose a gradient-based framework that crafts poisoning images with carefully manipulated feature information for each scenario. Using newly defined metrics at the class level, we demonstrate the effectiveness of the proposed class-oriented poisoning attacks on various models (e.g., LeNet-5, Vgg-9, and ResNet-50) over a wide range of datasets (e.g., MNIST, CIFAR-10, and ImageNet-ILSVRC2012) in an end-to-end training setting.
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