Rain often poses inevitable threats to deep neural network (DNN) based perception systems, and a comprehensive investigation of the potential risks of the rain to DNNs is of great importance. However, it is rather difficult to collect or synthesize rainy images that can represent all rain situations that would possibly occur in the real world. To this end, in this paper, we start from a new perspective and propose to combine two totally different studies, i.e., rainy image synthesis and adversarial attack. We first present an adversarial rain attack, with which we could simulate various rain situations with the guidance of deployed DNNs and reveal the potential threat factors that can be brought by rain. In particular, we design a factor-aware rain generation that synthesizes rain streaks according to the camera exposure process and models the learnable rain factors for adversarial attack. With this generator, we perform the adversarial rain attack against the image classification and object detection. To defend the DNNs from the negative rain effect, we also present a defensive deraining strategy, for which we design an adversarial rain augmentation that uses mixed adversarial rain layers to enhance deraining models for downstream DNN perception. Our large-scale evaluation on various datasets demonstrates that our synthesized rainy images with realistic appearances not only exhibit strong adversarial capability against DNNs, but also boost the deraining models for defensive purposes, building the foundation for further rain-robust perception studies.

翻译：降雨往往对深神经网络(DNN)基于神经网络(DNN)的感知系统构成不可避免的威胁,而全面调查雨水对DNN的潜在风险非常重要。然而,很难收集或合成能够代表真实世界中可能发生的所有降雨状况的雨量图像。为此,我们从新的角度出发,建议结合两种完全不同的研究,即雨成像合成和对抗性攻击。我们首先提出对抗性雨量攻击,在部署的DNN的指引下,我们可以模拟各种降雨情况,并揭示雨量可能造成的潜在威胁因素。特别是,我们设计一种因应因应因的雨量生成,根据摄像接触过程和模拟对抗性攻击的可学雨量因素来合成雨量记录。我们用这个发电机,对图像分类和物体探测进行对抗性雨量攻击。为了保护DNNN不受负雨效应的影响,我们还提出了一个防御性排水战略,为此我们设计了一种对抗性雨量扩增雨量战略,利用混合的对抗性雨层来增强对冲性雨量结构,用混合的雨量结构来增强下调模型,而不是为下游DNNNNR的图像展示展示能力。我们大型的大规模雨性模型展示了各种模型。