{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/shapeshifter-robust-physical-adversarial","title":"ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector","arxiv_id":"1804.05810","date":"2018-04-16","proceeding":null,"authors":["Shang-Tse Chen","Cory Cornelius","Jason Martin","Duen Horng Chau"],"abstract":"Given the ability to directly manipulate image pixels in the digital input\nspace, an adversary can easily generate imperceptible perturbations to fool a\nDeep Neural Network (DNN) image classifier, as demonstrated in prior work. In\nthis work, we propose ShapeShifter, an attack that tackles the more challenging\nproblem of crafting physical adversarial perturbations to fool image-based\nobject detectors like Faster R-CNN. Attacking an object detector is more\ndifficult than attacking an image classifier, as it needs to mislead the\nclassification results in multiple bounding boxes with different scales.\nExtending the digital attack to the physical world adds another layer of\ndifficulty, because it requires the perturbation to be robust enough to survive\nreal-world distortions due to different viewing distances and angles, lighting\nconditions, and camera limitations. We show that the Expectation over\nTransformation technique, which was originally proposed to enhance the\nrobustness of adversarial perturbations in image classification, can be\nsuccessfully adapted to the object detection setting. ShapeShifter can generate\nadversarially perturbed stop signs that are consistently mis-detected by Faster\nR-CNN as other objects, posing a potential threat to autonomous vehicles and\nother safety-critical computer vision systems.","url_abs":"http://arxiv.org/abs/1804.05810v3","url_pdf":"http://arxiv.org/pdf/1804.05810v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"shapeshifter-robust-physical-adversarial","repo_url":"https://github.com/shangtse/robust-physical-attack","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"shapeshifter-robust-physical-adversarial","repo_url":"https://github.com/AishrithRao/robust-physical-attack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"shapeshifter-robust-physical-adversarial","repo_url":"https://github.com/eetkim/physatt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.05810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05810"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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