Robust Physical Attack
Physical adversarial attack for fooling the Faster R-CNN object detector
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Robust Physical Attack | 82 | 4 years ago | bsd-3-clause | Jupyter Notebook | ||||||
| Physical adversarial attack for fooling the Faster R-CNN object detector | ||||||||||
| Tog | 74 | a year ago | 4 | Jupyter Notebook | ||||||
| Real-time object detection is one of the key applications of deep neural networks (DNNs) for real-world mission-critical systems. While DNN-powered object detection systems celebrate many life-enriching opportunities, they also open doors for misuse and abuse. This project presents a suite of adversarial objectness gradient attacks, coined as TOG, which can cause the state-of-the-art deep object detection networks to suffer from untargeted random attacks or even targeted attacks with three types of specificity: (1) object-vanishing, (2) object-fabrication, and (3) object-mislabeling. Apart from tailoring an adversarial perturbation for each input image, we further demonstrate TOG as a universal attack, which trains a single adversarial perturbation that can be generalized to effectively craft an unseen input with a negligible attack time cost. Also, we apply TOG as an adversarial patch attack, a form of physical attacks, showing its ability to optimize a visually confined patch filled with malicious patterns, deceiving well-trained object detectors to misbehave purposefully. | ||||||||||
| Daedalus Attack | 31 | 3 years ago | 1 | gpl-3.0 | Python | |||||
| The code of our paper: 'Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples', in Tensorflow. | ||||||||||
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Jupyter Notebook
Computer Vision
Convolutional Neural Networks
Attack
Object Detection
Faster Rcnn