We demonstrated that Non-Maximum Suppression (NMS), which is commonly used in object detection tasks to filter redundant detection results, is no longer secure. NMS has always been an integral part of object detection algorithms. Currently, Fully Convolutional Network (FCN) is widely used as the backbone architecture of object detection models. Given an input instance, since FCN generates end-to-end detection results in a single stage, it outputs a large number of raw detection boxes. These bounding boxes are then filtered by NMS to make the final detection results. In this paper, we propose an adversarial example attack which triggers malfunctioning of NMS in the end-to-end object detection models. Our attack, namely Daedalus, manipulates the detection box regression values to compress the dimensions of detection boxes. Henceforth, NMS will no longer be able to filter redundant detection boxes correctly. And as a result, the final detection output contains extremely dense false positives. This can be fatal for many object detection applications such as autonomous vehicle and smart manufacturing industry. Our attack can be applied to different end-to-end object detection models. Furthermore, we suggest crafting robust adversarial examples by using an ensemble of popular detection models as the substitutes. Considering that model reusing is commonly seen in real-world object detection scenarios, Daedalus examples crafted based on an ensemble of substitutes can launch attacks without knowing the details of the victim models. Our experiments demonstrate that our attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to 99.9 and reduces the mean average precision scores to 0, while maintaining a low cost of distortion on the original inputs.
Beyond the above attacks, some methods can generate robust adversarial examples which can fool real-world classifiers and detectors. For example, dense adversarial generation (DAG) algorithm was proposed to craft adversarial example for object detection and segmentation @cite_13 . craft robust adversarial examples that cause misclassification of object detector @cite_7 . @math was proposed to craft adversarial example of real-world road signs @cite_8 . Subsequently, @math was extended to attack YOLO-v2 @cite_53 . Recently, expectation over transformation (EoT) algorithm was proposed to synthesise robust adversarial examples @cite_49 @cite_2 . However, current adversarial examples only focus on triggering misclassification in machine learning classifiers object detectors. Our Daedalus attack creates adversarial examples that lead to malfunctioning of NMS, which is different from all the previous attacks.
@cite_7 @cite_8 @cite_53 @cite_49 @cite_2 @cite_13
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Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world--they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper we propose a new attack algorithm--Robust Physical Perturbations (RP2)-- that generates perturbations by taking images under different conditions into account. Our algorithm can create spatially-constrained perturbations that mimic vandalism or art to reduce the likelihood of detection by a casual observer. We show that adversarial examples generated by RP2 achieve high success rates under various conditions for real road sign recognition by using an evaluation methodology that captures physical world conditions. We physically realized and evaluated two attacks, one that causes a Stop sign to be misclassified as a Speed Limit sign in 100 of the testing conditions, and one that causes a Right Turn sign to be misclassified as either a Stop or Added Lane sign in 100 of the testing conditions. Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attacks generalize to the physical domain, to create perturbations on physical objects that fool image classifiers under a variety of real-world conditions. Such attacks pose a risk to deep learning models used in safety-critical cyber-physical systems. In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene. Improving upon a previous physical attack on image classifiers, we create perturbed physical objects that are either ignored or mislabeled by object detection models. We implement a Disappearance Attack, in which we cause a Stop sign to "disappear" according to the detector-either by covering thesign with an adversarial Stop sign poster, or by adding adversarial stickers onto the sign. In a video recorded in a controlled lab environment, the state-of-the-art YOLOv2 detector failed to recognize these adversarial Stop signs in over 85 of the video frames. In an outdoor experiment, YOLO was fooled by the poster and sticker attacks in 72.5 and 63.5 of the video frames respectively. We also use Faster R-CNN, a different object detection model, to demonstrate the transferability of our adversarial perturbations. The created poster perturbation is able to fool Faster R-CNN in 85.9 of the video frames in a controlled lab environment, and 40.2 of the video frames in an outdoor environment. Finally, we present preliminary results with a new Creation Attack, where in innocuous physical stickers fool a model into detecting nonexistent objects. In this paper, we proposed the first practical adversarial attacks against object detectors in realistic situations: the adversarial examples are placed in different angles and distances, especially in the long distance (over 20m) and wide angles 120 degree. To improve the robustness of adversarial examples, we proposed the nested adversarial examples and introduced the image transformation techniques. Transformation methods aim to simulate the variance factors such as distances, angles, illuminations, etc., in the physical world. Two kinds of attacks were implemented on YOLO V3, a state-of-the-art real-time object detector: hiding attack that fools the detector unable to recognize the object, and appearing attack that fools the detector to recognize the non-existent object. The adversarial examples are evaluated in three environments: indoor lab, outdoor environment, and the real road, and demonstrated to achieve the success rate up to 92.4 based on the distance range from 1m to 25m. In particular, the real road testing of hiding attack on a straight road and a crossing road produced the success rate of 75 and 64 respectively, and the appearing attack obtained the success rates of 63 and 81 respectively, which we believe, should catch the attention of the autonomous driving community. An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time. If adversarial examples existed that could fool a detector, they could be used to (for example) wreak havoc on roads populated with smart vehicles. Recently, we described our difficulties creating physical adversarial stop signs that fool a detector. More recently, produced a physical adversarial stop sign that fools a proxy model of a detector. In this paper, we show that these physical adversarial stop signs do not fool two standard detectors (YOLO and Faster RCNN) in standard configuration. 's construction relies on a crop of the image to the stop sign; this crop is then resized and presented to a classifier. We argue that the cropping and resizing procedure largely eliminates the effects of rescaling and of view angle. Whether an adversarial attack is robust under rescaling and change of view direction remains moot. We argue that attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - likely makes it difficult to make adversarial patterns. Finally, an adversarial pattern on a physical object that could fool a detector would have to be adversarial in the face of a wide family of parametric distortions (scale; view angle; box shift inside the detector; illumination; and so on). Such a pattern would be of great theoretical and practical interest. There is currently no evidence that such patterns exist.