论文标题
GAP ++：学会生成目标条件的对抗示例
GAP++: Learning to generate target-conditioned adversarial examples
论文作者
论文摘要
对抗性示例是扰动的输入，可能会对机器学习模型构成严重威胁。找到这些扰动是一项艰巨的任务，我们只能使用迭代方法进行遍历。为了计算效率，最近的作品使用对抗性生成网络直接对通用或图像依赖性扰动的分布进行建模。但是，这些方法生成扰动仅依赖于输入图像。在这项工作中，我们提出了一个更通用的框架，该框架会涉及目标条件的扰动，取决于输入图像和目标标签。与以前的单目标攻击模型不同，我们的模型可以通过学习攻击目标的关系和图像中的语义来进行目标条件攻击。使用MNIST和CIFAR10的数据集上的大量实验，我们表明我们的方法通过单个目标攻击模型实现了卓越的性能，并获得了较小的扰动规范的高愚蠢率。
Adversarial examples are perturbed inputs which can cause a serious threat for machine learning models. Finding these perturbations is such a hard task that we can only use the iterative methods to traverse. For computational efficiency, recent works use adversarial generative networks to model the distribution of both the universal or image-dependent perturbations directly. However, these methods generate perturbations only rely on input images. In this work, we propose a more general-purpose framework which infers target-conditioned perturbations dependent on both input image and target label. Different from previous single-target attack models, our model can conduct target-conditioned attacks by learning the relations of attack target and the semantics in image. Using extensive experiments on the datasets of MNIST and CIFAR10, we show that our method achieves superior performance with single target attack models and obtains high fooling rates with small perturbation norms.
