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Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks.
Distinctive image features from scale-invariant keypoints
Lowe, D. G · 2004
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Brown, T. B., Mané, D., Roy, A., Abadi, M., and Gilmer, J · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N., and Wagner, D · 2017
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No need to worry about adversarial examples in object detection in autonomous vehicles
Lu, J., Sibai, H., Fabry, E., and Forsyth, D · 2017
Earlier work this paper cites.
Standard detectors aren’t (currently) fooled by physical adversarial stop signs
Lu, J., Sibai, H., Fabry, E., and Forsyth, D · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Earlier work this paper cites.
A survey of product recognition in shelf images
Melek, C. G., Sonmez, E. B., and Albayrak, S · 2017
Earlier work this paper cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., and Qi, Y · 2017
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Amazon shoplifting punishment detection 2022
Amazon · 2018
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Dpatch: An adversarial patch attack on object detectors
Liu, X., Yang, H., Liu, Z., Song, L., Li, H., and Chen, Y · 2018
Earlier work this paper cites.
Yolov3: An incremental improvement
Redmon, J., and Farhadi, A · 2018
Earlier work this paper cites.
Physical adversarial examples for object detectors
Song, D., Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Tramer, F., Prakash, A., and Kohno, T · 2018
Earlier work this paper cites.
Cascade r-cnn: high quality object detection and instance segmentation
Cai, Z., and Vasconcelos, N · 2019
Earlier work this paper cites.
On evaluating adversarial robustness
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I., Madry, A., and Kurakin, A · 2019
Earlier work this paper cites.
MMDetection: Open mmlab detection toolbox and benchmark
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., Dai, J., Wang, J., Shi, J., Ouyang, W., Loy, C. C., and Lin, D · 2019
Cited alongside, same era.
Towards identification of packaged products via computer vision: Convolutional neural networks for object detection and image classification in retail environments
Fuchs, K., Grundmann, T., and Fleisch, E · 2019
Cited alongside, same era.
Neural style transfer: A review
Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., and Song, M · 2019
Cited alongside, same era.
On physical adversarial patches for object detection
Lee, M., and Kolter, Z · 2019
Cited alongside, same era.
A comprehensive survey on computer vision based approaches for automatic identification of products in retail store
A survey on adversarial attacks and defences
Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., and Mukhopadhyay, D · 2021
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Fashion meets computer vision: A survey
Cheng, W.-H., Song, S., Chen, C.-Y., Hidayati, S. C., and Liu, J · 2021
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Adversarial pixel masking: A defense against physical attacks for pre-trained object detectors
Chiang, P.-H., Chan, C.-S., and Wu, S.-H · 2021
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Super-Big Market-Data: A Case Study, Walkthrough Approach to Amazon Go Cashierless Convenience Stores
Green, K. M · 2021
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Naturalistic physical adversarial patch for object detectors
Hu, Y.-C.-T., Kung, B.-H., Tan, D. S., Chen, J.-C., Hua, K.-L., and Cheng, W.-H · 2021
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Adversarial yolo: Defense human detection patch attacks via detecting adversarial patches
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Santra, B., and Mukherjee, D. P · 2019
Cited alongside, same era.
Fooling automated surveillance cameras: adversarial patches to attack person detection
Thys, S., Van Ranst, W., and Goedemé, T · 2019
Cited alongside, same era.
Cascade rpn: Delving into high-quality region proposal network with adaptive convolution
Vu, T., Jang, H., Pham, T. X., and Yoo, C · 2019
Cited alongside, same era.
The limitations of adversarial training and the blind-spot attack
Zhang, H., Chen, H., Song, Z., Boning, D., Dhillon, I. S., and Hsieh, C.-J · 2019
Cited alongside, same era.
A survey on adversarial examples in deep learning
Chen, K., Zhu, H., Yan, L., and Wang, J · 2020
Cited alongside, same era.
Sentinet: Detecting localized universal attacks against deep learning systems
Chou, E., Tramer, F., and Pellegrino, G · 2020
Cited alongside, same era.
When explainability meets adversarial learning: Detecting adversarial examples using shap signatures
Fidel, G., Bitton, R., and Shabtai, A · 2020
Cited alongside, same era.
Pointrend: Image segmentation as rendering
Kirillov, A., Wu, Y., He, K., and Girshick, R · 2020
Cited alongside, same era.
Ji, N., Feng, Y., Xie, H., Xiang, X., and Liu, N · 2021
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An effective motion object detection using adaptive background modeling mechanism in video surveillance system
Kalli, S., Suresh, T., Prasanth, A., Muthumanickam, T., and Mohanram, K · 2021
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Vulnerable objects detection for autonomous driving: A review
Khatab, E., Onsy, A., Varley, M., and Abouelfarag, A · 2021
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Object detection method for grasping robot based on improved yolov5
Song, Q., Li, S., Bai, Q., Yang, J., Zhang, X., Li, Z., and Duan, Z · 2021
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Detectorguard: Provably securing object detectors against localized patch hiding attacks
Xiang, C., and Mittal, P · 2021
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You cannot easily catch me: A low-detectable adversarial patch for object detectors
Zhu, Z., Su, H., Liu, C., Xiang, W., and Zheng, S · 2021
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The translucent patch: A physical and universal attack on object detectors
Zolfi, A., Kravchik, M., Elovici, Y., and Shabtai, A · 2021
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Adversarial example detection for dnn models: A review and experimental comparison
Aldahdooh, A., Hamidouche, W., Fezza, S. A., and Déforges, O · 2022
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National retail security survey 2022
Federation, N. R · 2022
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Forbes shoplifting report 2022
Forbes · 2022
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Segment and complete: Defending object detectors against adversarial patch attacks with robust patch detection
Liu, J., Levine, A., Lau, C. P., Chellappa, R., and Feizi, S · 2022
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Denial-of-service attack on object detection model using universal adversarial perturbation
Shapira, A., Zolfi, A., Demetrio, L., Biggio, B., and Shabtai, A · 2022
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