Towards universal physical attacks on single object tracking. Jan 2021; 1236-1245; Li Ding; Yongwei Wang; Kaiwen Yuan; .

Towards universal physical attacks on single object tracking. Recent studies have shown that deep-learning .

Towards universal physical attacks on single object tracking. , random change in brightness, contrast, color, translations, rotation, sheering etc). Fooling the object detector, faster r-cnn, in the physical space. In this paper, we study the problem of physical attacks on object detectors. We identify that three major challenges, i. v35i2. , 2020) demonstrated the possibility to craft adversaries in the rst frame of a video clip, forcing trackers, especially SiamRPN-based ones to lose the target in subsequent frames. Nov 30, 2023 · Recent years have witnessed significant advancements in deep learning-based 3D object detection, leading to its widespread adoption in numerous applications. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. These attacks have been heavily investigated in the RGB image domain and more recently in the point cloud domain Jan 26, 2021 · Abstract and Figures. It has large performance gains (>5 points) on DeepLesion, Comic, and Clipart. , object occlusion and deformation. The universal detector performs compara-bly to the single-domain detector bank, with 10 times fewer parameters. Specifically, we first make observations on the patch optimization process of the existing method and propose an enhanced attack framework by Jan 11, 2024 · Recent research has revealed that single object tracking (SOT) is susceptible to adversarial examples, with even small perturbations added to video frames leading to tracking failure. 16211 Corpus ID: 235306169; Towards Universal Physical Attacks on Single Object Tracking @inproceedings{Ding2021TowardsUP, title={Towards Universal Physical Attacks on Single Object Tracking}, author={Li Ding and Yongwei Wang and Kaiwen Yuan and Minyang Jiang and Ping Wang and Hua Huang and Z. We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. 3D object tracking has great potential applications in computing intensive cyber-physical systems, particularly auto-nomic driving. Specifically, we generate an adversarial patch to attack. 14938 Google Figure 1. Ding et al. The results of attacking Volvo XC60 (top row) and Volkswagen Tiguan (bottom row). 877–894. In contrast to existing black-box adversarial attack methods that deal with static images for image 7. , 2020 ) demonstrated the possibility to craft Overview the threshold or the iteration reaches the maximum. The red Jan 1, 2023 · Here we made the first step towards physically feasible adversarial attacks against visual tracking in real scenes with a universal patch to camouflage single object trackers. Jane Wang 1236-1245 Universal Adversarial Attack. Jane Wang}, booktitle={AAAI Conference on Artificial Intelligence}, year Mar 31, 2023 · In order to attract attentions to this potential risk and facilitate the study of robustness in point cloud tracking, we introduce a novel transferable attack network (TAN) to deceive 3D point cloud tracking. Specifically, TAN consists of a 3D adversarial generator, which is trained with a carefully designed multi-fold drift (MFD) loss. While those earlier attacks are based on methods that generate a perturbation for a single image, Moosavi-Dezfooli et al. —Recently, adversarial examples against object detection have been widely studied. - "Towards Universal Physical Attacks on Jan 26, 2021 · Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models. This method Oct 31, 2022 · Huang et al. SPARK: spatial-aware online incremental attack against visual tracking; L. This paper presents a novel tracker hijacking attack against the multi-target tracking algorithm employed by real-world autonomous driving systems, which controls the bounding box of object detection to spoof the multiple object tracking process. Current methods usually superimpose perturbation maps on the input images, and advocate blinding the tracker via occluding the real targets to achieve attack effect. May 1, 2021 · The adversarial patch attack has become one of the most practical threat models for computer vision models in the physical world. Apr 30, 2023 · The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. Concretely, UPC crafts Mar 27, 2021 · In this paper, we propose a decision-based black-box attack method for visual object tracking. Jane Wang}, booktitle={AAAI Conference on Artificial Intelligence}, year={2021} } DOI: 10. By imposing the seman- object from being detected, or fooling detectors to output tic constraint (Sec. , & Yang, X. Conclusion. , video surveillance, visual navigation. December 2021. Li Ding, Yongwei Wang, Kaiwen Yuan, Minyang Jiang, Ping Wang, Hua Huang, Z. 1236-1245. Experiments on the KITTI dataset demonstrate that the performance of 3D object tracking can be significantly degraded by the proposed method. "Towards Universal Physical Attacks on Single Object Tracking. Object tracking is one of the foremost assignments in computer vision that has numerous commonsense applications such as traffic monitoring, robotics, autonomous vehicle tracking, and so on. Most adversarial methods advocate generating perturbations for each video frame, but frequent attacks may increase the computational load and the risk of exposure. Image-agnostic attack, i. Robust physical-world attacks on machine learning models. AAAI. delved into physical attacks on object detectors in the wild by developing a universal camouflage for object categories. - "Universal Physical Camouflage Attacks on Object Detectors" Jan 31, 2022 · Specifically, we propose a simple yet effective few-shot backdoor attack (FSBA) that optimizes two losses alternately: 1) a \emph {feature loss} defined in the hidden feature space, and 2) the standard \emph {tracking loss}. These methods generate video-specific or target-specific perturbation to deceive the trackers through a frame-by-frame online paradigm. Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to Mar 16, 2022 · Towards universal physical attacks on single object tracking. - "Universal Physical Camouflage Attacks on Object Detectors" Towards robust lidar-based perception in autonomous driving: General black-box adversarial sensor attack and countermeasures. DNNs have Towards the unification of object tracking, we propose Unicorn, a single network architecture to solve four tracking tasks. Then a feature interaction module is exploited to build pixel-wise correspondence between two Dec 1, 2023 · Adversarial attacks in visual object tracking aim to drop tracking performance through injecting imperceptible perturbations to the input of the tracker. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. This paper presents the first comprehensive evaluation and analysis of the Figure 5. - "Universal Physical Camouflage Attacks on Object Detectors" To address the challenge of the adversarial attack in remote sensing object tracking we mentioned above, we propose a novel one-shot adversarial attack method for remote sensing object tracking Oct 1, 2023 · Nevertheless, existing attack methods for object tracking are limited to Siamese networks, with other types of trackers being infrequently targeted. 12. Guo et al. Digital Attacks. - "Universal Physical Camouflage Attacks on Object Detectors" Sep 24, 2022 · 3. " AAAI (2021). TOWARDS UNIVERSAL PHYSICAL ATTACKS ON CASCADED CAMERA-LIDAR 3D We plan to learn a single adversarial object that is placed on top of a car in a 3D scene, with the goal that this adversar- Sep 10, 2019 · In this paper, we study physical adversarial attacks on object detectors in the wild. Fundamentally This paper proposes a novel one-shot adversarial attack method to generate adversarial examples for free-model single object tracking, where merely adding slight perturbations on the target patch in the initial frame causes state-of-the-art trackers to lose the target in subsequent frames. To solve above problems and track the target accurately and Nov 7, 2022 · Towards universal physical attacks on single object tracking. Visual object tracking is an important task in computer vision, which has many real-world applications, e. , moving object discovery, rich temporal variation exploitation, and online update, are the central causes of the performance bottleneck of existing unsupervised trackers. However, Object tracking based on deep neural networks is vulnerable to adversarial examples. Fundamentally different from physical object detection, the essence of single object tracking lies in the feature matching between the search image and templates, and we May 18, 2021 · Here we made the first step towards physically feasible adversarial attacks against visual tracking in real scenes with a universal patch to camouflage single object trackers. In AAAI Conference on Artificial Intelligence, 2021. However, previous work only generates the video-specific perturbations, which restricts its application scenarios. proposed to attack a model. However, it is difficult for these attacks to have an impact on Dec 1, 2023 · IoU attack: towards temporally coherent black-box adversarial attack for visual object tracking; Q. The table reports the percentage of performance drop in tracking with patches from: Random, Dilation and Shrinking attacks, respectively. In 29th {USENIX} Security Symposium ( {USENIX} Security 20). We propose an efficient adversarial patch attack against a specific target class in the object detection model, called Invisibility Patch. Towards universal physical attacks on single object tracking; S. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment Towards Universal Physical Attacks on Single Object Tracking L Ding, Y Wang, K Yuan, M Jiang, P Wang, H Huang, ZJ Wang 2021 Proceedings of the AAAI Conference on Artificial Intelligence 35 , 2021 Figure 2: Overview of the proposed attack pipeline. Efficient adversarial attacks for visual object tracking; X. Traditional object tracking models fall into two categories: generative and discriminative models. Jan 31, 2022 · Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics. As 3D object detectors become increasingly crucial for security-critical tasks, it is imperative to understand their robustness against adversarial attacks. The generated universal perturbation is designed to be aware of the topology of the targeted tracking template during its construction and application, thus leading to superior attack performance. extended adversarial attacks to the physical world by solving an optimization problem that minimizes the target object’s probability in the output of the object detection model, thereby hiding the target object. Here we extend this definition to the physical domain and define instance-agnostic perturbations as universal physical attacks This work proposes an attention-enhanced one-shot adversarial attack method for UAV remote sensing object tracking, which perturbs only the template frame and generates adversarial samples offline, and validate the effectiveness of the method against popular trackers such as SiamRPN, DaSiam RPN, and SienRPN++ on the UAV123 remote sensing dataset. 4points with a 5-fold parameter decrease. , army camouflage cloths, pilot cap and snow goggles); Natural: humans with natural images as camouflage patterns. Sep 14, 2023 · Wang et al. e. May 18, 2021 · Table 2: Quantitative performance evaluation of the proposed attacks on SiamMask (#1) and SiamRPN++ (#2) with person, car and bottle categories. MSANet: Xuesong Chen, Canmiao Fu, Feng Zheng, Yong Zhao, Hongsheng Li, Ping Luo, Guo-Jun Qi. Dec 1, 2021 · Universal Adversarial Attack Against 3D Object Tracking. , Song, Y. Thedomain-attentiveuniversaldetector(auni-versal+DAo)improvesbaselineperformanceby4. This setting is challenging for two reasons: the perturbation region is very small and the perturbation is not differentiable. In addition, existing attacks are difficult to implement in reality due to the real-time of tracking and the re Mar 1, 2024 · Despite its utility, object tracking poses several challenges such as occlusion, scale change, background clutter, target deformation, and motion blur. Physically feasible. Even for unconstrained patterns, human observer can relate the generated camouflage patterns to the targeted label. It takes the reference frame and the current frame as the inputs and produces their visual features by a weight-shared backbone. Oct 28, 2022 · Ding, L. Jan 26, 2021 · The proposed universal multi-modal attack was successful in reducing the model’s ability to detect a car by nearly 73% and can aid in the understanding of what the cascaded RGB-point cloud DNN learns and its vulnerability to adversarial attacks. g. Fundamentally different from physical object detection, the essence Mar 14, 2022 · An offline universal adversarial attack called Efficient Universal Shuffle Attack, which takes only one perturbation to cause the tracker malfunction on all videos and can significantly reduce the performance of state-of-the-art trackers on OTB2015 and VOT2018. arXiv:2103. The 1st and 3rd rows show visual examples of the proposed dilation and shrinking attacks on “person”. Practical physical attacks present more challenges than dig-ital attacks on the trackers. [] proposed universal adversarial perturbations (UAPs), which enable any image that is blended with the UAP to fool a DNN. Although many recent works have attacked the object detection Towards Universal Physical Attacks on Single Object Tracking, Li Ding, Yongwei Wang, Kaiwen Yuan, Minyang Jiang, Ping Wang, Hua Huang, Z. Jan 2021; 1236-1245; Li Ding; Yongwei Wang; Kaiwen Yuan; Towards universal physical attacks on single object tracking. The loss function consists of adversarial loss, width loss, and non-printable loss. Given a randomly-initialized patch and the training streams, firstly the patch is transformed randomly (e. Recently, differentiable renderers were used to make adver-sarial attacks by altering the 3D geometry of an object or its lighting conditions and then rendering them to images for an Figure 4: Illustration of the effectiveness of the generated patch. We show that, once the backdoor is embedded into the target model by our FSBA, it can trick the model to lose track of We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. Iou attack: Towards temporally coherent black-box adversarial attack for visual object tracking. "Visual Tracking via Hierarchical Deep Reinforcement Learning. 1246-1254. In this paper, we propose a novel one-shot adversarial attack method to generate adversarial examples for free-model single ob- ject tracking, where merely adding slight perturbations on the target patch in the initial frame causes state-of-the-art trackers to lose the target in subsequent frames. May 1, 2020 · [Show full abstract] attacks against visual tracking in real scenes with a universal patch to camouflage single object trackers. PDF Sep 1, 2023 · Computer Science. Virtual scenes (i. Network and Distributed System Security (NDSS) Symposium Towards Universal Physical Attacks on Single Object Tracking, Li Ding, Yongwei Wang, Kaiwen Yuan, Minyang Jiang, Ping Wang, Hua Huang, Z. PDF; Modeling the Probabilistic Distribution of Unlabeled Data for One-shot Medical Image Segmentation. Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. 1109/HPCC-DSS-SmartCity-DependSys53884. We present a method for creating inconspicuous-looking textures that, when Jan 26, 2021 · We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. Jane Wang . 1 Adversarial Attacks. Paper. However, most of the existing methods are online attack methods. Conference: 2021 IEEE 23rd Int Conf on High Performance Computing A Unified and Effective Network, named UEN, to attack visual object tracking models, which is able to attack many state-of-the-art trackers effectively and outperform the introduced baseline in terms of attacking ability and attacking efficiency. Visual object tracking also has many challenges, e. The 2nd row shows the comparison in IoU prediction between clean and attacks over time. , AttackScenes) are shown in the first row, including indoors and outdoors environments. Also, universal attacks were stud-ied where an attack is not just trained on a single image [13]. : Towards universal physical attacks on single object tracking. 3. 1 Objective Function. Expand. Figure 3. Oct 31, 2022 · physical attacks on object detectors in the wild by de veloping a universal camouflage f or object categories. 2021. Jane Wang. Nov 7, 2022 · Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. Our goal is to attack object detectors by either hiding the Attacking in Physical Space. Different researches have been tried later a long time, but since of diverse Jan 26, 2021 · We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible Sep 10, 2019 · Figure 4. May 28, 2021 · Towards Universal Physical Attacks on Single Object Tracking Li Ding, Yongwei Wang, Kaiwen Yuan, Minyang Jiang, Ping Wang, Hua Huang, Z. Yin et al. In Thirty Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames. Jul 20, 2018 · This work improves upon a previous physical attack on image classifiers, and creates perturbed physical objects that are either ignored or mislabeled by object detection models, and implements a Disappearance Attack, which causes a Stop sign to "disappear" according to the detector. 3/7/8-Patterns: according to the heatmaps of detection models, we pre-define 3/7/8 regions on human accessories to paint Jul 1, 2023 · Here we made the first step towards physically feasible adversarial attacks against visual tracking in real scenes with a universal patch to camouflage single object trackers. However, previous research demonstrates that adversarial examples pose a significant threat to remote sensing imagery. - "Universal Physical Camouflage Attacks on Object Detectors" Apr 25, 2022 · Single Object Tracking Research: A Survey. Recently, differentiable renderers were used to make adver-sarial attacks by altering the 3D geometry of an object or its lighting conditions and then rendering them to images for an Jul 1, 2023 · Adversarial attacks in visual object tracking aims to fool trackers via injecting invisible perturbations for the video frames. 49. Yuhang Ding, Xin Yu, Yi Yang. This article first explores the impact of adversarial Universal physical camouflage attacks on object detectors. The generated camouflage patterns fool detectors to misrecognize the car as bird. 1609/aaai. Google Scholar; Jia, S. The camouflage is generated by FR-VGG16. Examples of pattern schemes in the virtual scenes experiment. DOI: 10. Here we made the first step towards physically feasible adversarial attacks against visual tracking in real scenes with a universal patch to camouflage single object trackers. We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self Sep 10, 2019 · Figure 2. **Visual Object Tracking** is an important research topic in computer vision, image understanding and pattern recognition. “↓” denotes performance drop and larger values are preferred. Google Scholar Cross Ref; Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song. Consequently, robust attack efficiency of the tracker is necessary to overcome these challenges. Universal Adversarial Attack. A Unified and Effective Network, named UEN, to attack visual object tracking models, which is able to attack many state-of-the-art trackers effectively and outperform the introduced baseline in terms of attacking ability and attacking efficiency. Specifically, we propose UPC to gen- erate universal camouflage patterns which hide a category of objects from being detected or to misdetect objects as the target label by state-of-the-art object detectors. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent physical attacks on object detectors in the wild by developing a universal camouage for object categories. The symbol “↓” denotes performance drop and larger values indicate stronger attacks. - "Towards Universal Physical Attacks on Single Object Tracking" 【ER】Physical adversarial attack on vehicle detector in the carla simulator: paper|code: Black-box-Object Detection: Camouflage: Light-Head RCNN: Nontargeted: Static: 3D: 2020-CVPR 【UPC】Universal physical camouflage attacks on object detectors: paper|code: White-box: EOT: Object Detection: Camouflage: Faster RCNN: Targeted Nontargeted Apr 24, 2019 · While the Expectation Over Transformation (EOT) algorithm is used to generate physical adversaries that fool tracking models when imaged under diverse conditions, the impacts of different scene variables are compared to find practical attack setups with high resulting adversarial strength and convergence speed. DNNs have achieved high performance in 3D object detection, but they are known to be vulnerable to adversarial attacks. In order to expand the usage of adversarial attacks in object tracking, we propose a model-free black-box framework for learning to generate universal and sparse adversarial examples (USAE) for Figure 7. Previous works mostly craft instance-dependent perturbations only for rigid or planar objects. Examples of virtual scene experiments. PDF. TLDR. Nms by representative region: Towards crowded pedestrian detection by proposal pairing. Here we extend this definition to the physical domain and define instance-agnostic perturbations as universal physical attacks 4. Liang et al. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2 Figure 8. , Ma, C. These attacks have been heavily investigated in the RGB image domain and more recently in the point cloud domain . To ensure that the background image has good attack performance and reproducibility in the physical world, we carefully design a loss function to optimize the universal background image. 00032. , et al. Code. 2), the generated camouflage patterns the targeted label. As elaborated below, we address three challenges: (1) physically realizable; (2) universal to diverse instances; and (3) robust to physical conditions and tracker re-initialization. Universal Physical Attacks. (a) Physical attacks (UPC) in virtual scenes and (b) Physical attacks (UPC) in real world. This paper proposes to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC), which crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors. Bounding boxes in red depict the initialization positions while the blue ones display predicted positions after our attacks. , universal adversarial attack [25, 13], is defined as an attack which is able to fool different images with a sin-gle global pattern in the digital domain. Recent research has extended adversarial attacks to object-tracking tasks. 720–729). The second rows shows results captured under various physical conditions with different pattern schemes. , 2020 ) demonstrated the possibility to craft adversaries in the first frame of a video clip, forcing trackers, especially SiamRPN-based ones to lose the target in subsequent frames. Visual Tracking via Hierarchical Deep Reinforcement Learning, Dawei Zhang, Zhonglong Zheng, Riheng Jia, Minglu Li [2022] TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems [Generative] [2022][39:fire:] Physical attack on monocular depth estimation with optimal adversarial patches [Optimization-based] [2021][62:fire:] Naturalistic physical adversarial patch for object detectors [Generative] proposed to attack a model. Column 2-4 display results with camouflage patterns under different viewing conditions. (a) training the camouflage patterns in digital space; (b) attacking the target in physical space. Highly Influential. - "Universal Physical Camouflage Attacks on Object Detectors" Jul 1, 2023 · 2020. (2021). Sep 10, 2019 · TLDR. Here we extend this definition to the physical domain and define instance-agnostic perturbations as universal physical attacks Jun 6, 2023 · In one pixel attacks, an attacker aims to fool an image classifier by modifying a single pixel. This paper proposes a novel one-shot adversarial attack method to generate adversarial examples for free-model single object tracking, where merely adding slight perturbations on the target patch in the initial frame causes state-of-the-art trackers to lose the target in subsequent frames. Column 1 shows detection results with natural patterns. Jane Wang}, booktitle={AAAI Conference on Artificial Intelligence}, year Notes we focus on the single-model transfer-based black-box attack on object detection, utilizing only one model to achieve a high-transferability adversarial attack on multiple black-box detectors. pdf. Experimental results in (a) stationary testing and (b) motion testing. Table 3: Quantitative performance evaluation in physical attacks. Initially, attacks in the digital domain aimed at fooling classification models were introduced [9, 22]. 2021. Nov 7, 2022 · AttrackZone is presented, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. 720--729. One-shot attack (Chen et al. Original: humans without camouflage patterns; Naive: humans with simple camouflages (i. Mar 17, 2023 · 2. In this paper, we propose to learn an Unsupervised Single Object Tracker (USOT) from scratch. Yiren Zhao, Ilia Shumailov, Robert Mullins, and Ross Anderson. Recent studies have shown that deep-learning Towards Universal Physical Attacks on Single Object Tracking. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC). Generated camouflage patterns are semantically meaningful. Google Scholar Cross Ref; Xin Huang, Zheng Ge, Zequn Jie, and Osamu Yoshie. Previous works generate adversarial examples by applying perturbations to samples, which suffer from the generation of an individual adversarial perturbation for each We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. Adversarial Loss. 2020. PACNet: Dawei Zhang, Zhonglong Zheng, Riheng Jia, Minglu Li. Jane Wang Visual Tracking via Hierarchical Deep Reinforcement Learning , Dawei Zhang, Zhonglong Zheng, Riheng Jia, Minglu Li [ Paper ] Universal physical camouflage attacks on object detectors. DNNs have achieved high performance in 3D object detection, but they are known to be vulnerable to DOI: 10. The overall pipeline of UPC. yr qz eo rs sg et ij ws fb sv