论文标题
Imagenet-Patch：用于基准测试机器学习鲁棒性针对对抗贴片的数据集
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
论文作者
论文摘要
对抗贴片是在输入图像中优化的连续像素块，该贴图会导致机器学习模型将其错误分类。但是，它们的优化是计算要求的，需要仔细的高参数调整，可能会导致次优鲁棒性评估。为了克服这些问题，我们提出了Imagenet-Patch，这是针对对抗贴片的基准机器学习模型的数据集。它由一组贴片组成，以优化以跨不同模型的概括，并在用仿射变换进行预处理后，很容易适用于Imagenet数据。此过程可以实现近似但更快的鲁棒性评估，从而利用了对抗扰动的转移性。我们通过测试计算的贴片对127个模型的有效性来展示该数据集的有用性。最后，我们讨论了如何将数据集用作鲁棒性的基准，以及如何将我们的方法论推广到其他领域。我们在https://github.com/pralab/imagenet-patch上开源数据集和评估代码。
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches, optimized to generalize across different models, and readily applicable to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations. We showcase the usefulness of this dataset by testing the effectiveness of the computed patches against 127 models. We conclude by discussing how our dataset could be used as a benchmark for robustness, and how our methodology can be generalized to other domains. We open source our dataset and evaluation code at https://github.com/pralab/ImageNet-Patch.
