论文标题
大转弯的小雾
A Little Fog for a Large Turn
论文作者
论文摘要
小型，精心制作的扰动称为对抗性扰动很容易欺骗神经网络。但是，这些扰动在很大程度上是加性的，并且没有自然发现。我们将注意力转向自主导航领域，其中不利天气条件（例如雾）对这些系统的预测产生了巨大影响。这些天气状况能够像天然对手一样行动，可以帮助测试模型。为此，我们介绍了一种对抗性扰动的一般概念，可以使用生成模型创建，并提供一种受周期一致的生成对抗网络启发的方法，以生成给定图像的对抗天气条件。我们的配方和结果表明，这些图像为自动导航模型中使用的转向模型提供了合适的测试台。我们的工作还提出了基于感知相似性的对抗性扰动的更自然和一般的定义。
Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. We turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the predictions of these systems. These weather conditions are capable of acting like natural adversaries that can help in testing models. To this end, we introduce a general notion of adversarial perturbations, which can be created using generative models and provide a methodology inspired by Cycle-Consistent Generative Adversarial Networks to generate adversarial weather conditions for a given image. Our formulation and results show that these images provide a suitable testbed for steering models used in Autonomous navigation models. Our work also presents a more natural and general definition of Adversarial perturbations based on Perceptual Similarity.
