Adversarial Examples for Semantic Segmentation and Object Detection
Switch branches/tags
Nothing to show
Clone or download
Latest commit 55505e2 Jan 30, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
caffe @ ed05614 add caffe submodule Aug 7, 2017
code add .gitignore and fix aspect ratio Aug 8, 2017
data first version files Aug 7, 2017
fetch_data fix code alignment Aug 7, 2017
functions add function explaination Aug 8, 2017
prototxt fix code alignment Aug 7, 2017
.gitignore add .gitignore and fix aspect ratio Aug 8, 2017
.gitmodules add caffe submodule Aug 7, 2017
LICENSE Create LICENSE Aug 8, 2017
README.MD Update README.MD Jan 30, 2018
demo_image.png Add files via upload Dec 3, 2017

README.MD

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}
}