Adversarial Examples that Fool Detectors
arXiv:1712.02494
Abstract
An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time. To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors. If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles. In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO. The existence of such examples is surprising, as 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 - makes it quite likely that adversarial patterns are strongly disrupted. We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed. We also show that our construction yields physical objects that are adversarial.
Follow up paper for adversarial stop signs. Submitted to CVPR 2018
References in corpus (8)
- Towards Deep Neural Network Architectures Robust to Adversarial Examples
- YOLO9000: Better, Faster, Stronger
- Countering Adversarial Images using Input Transformations
- Defensive Distillation is Not Robust to Adversarial Examples
- NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
- An Implementation of Faster RCNN with Study for Region Sampling
- Universal adversarial perturbations
- Standard detectors aren't (currently) fooled by physical adversarial stop signs
Cited by in corpus (4)
- Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations
- Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
- Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
- Towards Adversarially Robust Object Detection