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Adversarial Examples for Semantic Segmentation and Object Detection

This repo privdes a simple algorithm, Dense Adversary Generation (DAG), to find adversarial examples for semantic segmentation and object detection (https://arxiv.org/abs/1703.08603). An adversarial example which let both the detection network and the segmentation network fail is shown below:

Demo Image

Code

generate_config.m

The config arguments:

  • model_select: models used for generating adversarial examples.
  • MAX_ITER: max iteration number for generating adversarial examples (default = 150 for detection, = 200 for segmentation).
  • step-length: max pixel value change at each iteration (default = 0.5)
  • net_model: network model where the last layer (loss) is removed and backward is enabled.
  • net_weight: network weight
  • for segmentation
    • shape: the segmenation shape of the adversarial example: circle, diamond, strip.

demo.m

A simple demo which computes the adversarial examples for object detection and semantic segmentation algorithms, the output includes:

  1. visualization of segmentation or detection result of the adversarial example;
  2. visualization of original image X, adversarial examples X + r, adversarial perturbation r.

Software Requirements

  1. Caffe should use the version from Microsoft (https://github.com/Microsoft/caffe) which supports the roi_pooling_layer
  2. Caffe must be complied with 'matcaffe'

Citing DAG

If you find DAG is useful in your research, please consider citing:

@inproceedings{xie2017adversarial,
    title={Adversarial Examples for Semantic Segmentation and Object Detection},
    author={Xie, Cihang and Wang, Jianyu and Zhang, Zhishuai and Zhou, Yuyin and Xie, Lingxi and Yuille, Alan},
    Booktitle={International Conference on Computer Vision},
    year={2017},
    organization={IEEE}
}

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