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Register a new userDPA: Learning Robust Physical Adversarial Camouflages for Object Detectors
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Added by
Zhisong Pan
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Adversarial attacks are feasible in the real world for object detection. However, most of the previous works have tried to learn patches applied to an object to fool detectors, which become less effective or even ineffective in squint view angles. To address this issue, we propose the Dense Proposals Attack (DPA) to learn robust, physical and targeted adversarial camouflages for detectors. The camouflages are robust because they remain adversarial when filmed under arbitrary viewpoint and different illumination conditions, physical because they function well both in the 3D virtual scene and the real world, and targeted because they can cause detectors to misidentify an object as a specific target class. In order to make the generated camouflages robust in the physical world, we introduce a combination of viewpoint shifts, lighting and other natural transformations to model the physical phenomena. In addition, to improve the attacks, DPA substantially attacks all the classifications in the fixed region proposals. Moreover, we build a virtual 3D scene using the Unity simulation engine to fairly and reproducibly evaluate different physical attacks. Extensive experiments demonstrate that DPA outperforms the state-of-the-art methods significantly, and generalizes well to the real world, posing a potential threat to the security-critical computer vision systems.
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In this paper, we propose a natural and robust physical adversarial example attack method targeting object detectors under real-world conditions. The generated adversarial examples are robust to various physical constraints and visually look similar to the original images, thus these adversarial examples are natural to humans and will not cause any suspicions. First, to ensure the robustness of the adversarial examples in real-world conditions, the proposed method exploits different image transformation functions, to simulate various physical changes during the iterative optimization of the adversarial examples generation. Second, to construct natural adversarial examples, the proposed method uses an adaptive mask to constrain the area and intensities of the added perturbations, and utilizes the real-world perturbation score (RPS) to make the perturbations be similar to those real noises in physical world. Compared with existing studies, our generated adversarial examples can achieve a high success rate with less conspicuous perturbations. Experimental results demonstrate that, the generated adversarial examples are robust under various indoor and outdoor physical conditions, including different distances, angles, illuminations, and photographing. Specifically, the attack success rate of generated adversarial examples indoors and outdoors is high up to 73.33% and 82.22%, respectively. Meanwhile, the proposed method ensures the naturalness of the generated adversarial example, and the size of added perturbations is much smaller than the perturbations in the existing works. Further, the proposed physical adversarial attack method can be transferred from the white-box models to other object detection models.
Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection. Noting that most state-of-the-art object detectors benefit from fine-tuning a pre-trained classifier, we first study how the classifiers gains from various data augmentations transfer to object detection. The results are discouraging; the gains diminish after fine-tuning in terms of either accuracy or robustness. This work instead augments the fine-tuning stage for object detectors by exploring adversarial examples, which can be viewed as a model-dependent data augmentation. Our method dynamically selects the stronger adversarial images sourced from a detectors classification and localization branches and evolves with the detector to ensure the augmentation policy stays current and relevant. This model-dependent augmentation generalizes to different object detectors better than AutoAugment, a model-agnostic augmentation policy searched based on one particular detector. Our approach boosts the performance of state-of-the-art EfficientDets by +1.1 mAP on the COCO object detection benchmark. It also improves the detectors robustness against natural distortions by +3.8 mAP and against domain shift by +1.3 mAP. Models are available at https://github.com/google/automl/tree/master/efficientdet/Det-AdvProp.md
In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected objects in the image. That is, we can place the patch anywhere in the image, causing all existing objects in the image to be missed entirely by the detector, even those far away from the patch itself. This in turn opens up new lines of physical attacks against object detection systems, which require no modification of the objects in a scene. A demo of the system can be found at https://youtu.be/WXnQjbZ1e7Y.
Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input. Inputs with elaborately designed perturbations that can fool deep learning models are called adversarial examples, and they have drawn great concerns about the safety of deep neural networks. Object detection algorithms are designed to locate and classify objects in images or videos and they are the core of many computer vision tasks, which have great research value and wide applications. In this paper, we focus on adversarial attack on some state-of-the-art object detection models. As a practical alternative, we use adversarial patches for the attack. Two adversarial patch generation algorithms have been proposed: the heatmap-based algorithm and the consensus-based algorithm. The experiment results have shown that the proposed methods are highly effective, transferable and generic. Additionally, we have applied the proposed methods to competition Adversarial Challenge on Object Detection that is organized by Alibaba on the Tianchi platform and won top 7 in 1701 teams. Code is available at: https://github.com/FenHua/DetDak
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