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：

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.

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.

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'

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}
}