Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a deep neural network image classifier, as demonstrated in prior work. In this work, we tackle the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. In this showcase, we will demonstrate the first robust physical adversarial attack that can fool a state-of-the-art Faster R-CNN object detector. Specifically, we will show various perturbed stop signs that will be consistently mis-detected by an object detector as other target objects. The audience can test in real time the robustness of our adversarially crafted stop signs from different distances and angles. This work is a collaboration between Georgia Tech and Intel Labs and is funded by the Intel Science & Technology Center for Adversary-Resilient Security Analytics at Georgia Tech.
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ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we propose ShapeShifter, an attack that tackles the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. We show that the Expectation over Transformation technique, which was originally proposed to enhance the robustness of adversarial perturbations in image classification, can be successfully adapted to the object detection setting. ShapeShifter can generate adversarially perturbed stop signs that are consistently mis-detected by Faster R-CNN as other objects, posing a potential threat to autonomous vehicles and other safety-critical computer vision systems.
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- Award ID(s):
- 1704701
- NSF-PAR ID:
- 10095832
- Date Published:
- Journal Name:
- Joint European Conference on Machine Learning and Knowledge Discovery in Databases
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-10925-7_4
