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.

提出一种更通用的框架，该框架可以推断基于输入图像和目标标签的目标条件扰动，通过学习攻击目标与图像中的语义关系。在MNIST和CIFAR10数据集的广泛实验中，该方法实现了超越单目标攻击模型的优异性能，并以小扰动范数获得高愚弄率。

GAP++: 学习生成目标条件下对抗性样本