Previous work has shown that Deep Neural Networks (DNNs), including those currently in use in many fields, are extremely vulnerable to maliciously crafted inputs, known as adversarial examples. Despite extensive and thorough research of adversarial examples in many areas, adversarial 3D data, such as point clouds, remain comparatively unexplored. The study of adversarial 3D data is crucial considering its impact in real-life, high-stakes scenarios including autonomous driving. In this paper, we propose a novel adversarial attack against PointNet++, a deep neural network that performs classification and segmentation tasks using features learned directly from raw 3D points. In comparison to existing works, our attack generates not only adversarial point clouds, but also robust adversarial objects that in turn generate adversarial point clouds when sampled both in simulation and after construction in real world. We also demonstrate that our objects can bypass existing defense mechanisms designed especially against adversarial 3D data.
Adversarial examples that can fool deep neural network (DNN) models in computer vision present a growing threat. The current methods of launching adversarial attacks concentrate on attacking image classifiers by adding noise to digital inputs. The problem of attacking object detection models and adversarial attacks in physical world are rarely touched. Some prior works are proposed to launch physical adversarial attack against object detection models, but limited by certain aspects. In this paper, we propose a novel physical adversarial attack targeting object detection models. Instead of simply printing images, we manufacture real metal objects that could achieve the adversarial effect. In both indoor and outdoor experiments we show our physical adversarial objects can fool widely applied object detection models including SSD, YOLO and Faster R-CNN in various environments. We also test our attack in a variety of commercial platforms for object detection and demonstrate that our attack is still valid on these platforms. Consider the potential defense mechanisms our adversarial objects may encounter, we conduct a series of experiments to evaluate the effect of existing defense methods on our physical attack.
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