Fooling Code

This is the code base used to reproduce the "fooling" images in the paper:

Anh Nguyen, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." arXiv preprint arXiv:1412.1897 (2014).

If you use this software in an academic article, please cite:

@article{nguyen2014deep,
  title={Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images},
  author={Nguyen, Anh and Yosinski, Jason and Clune, Jeff},
  journal={arXiv preprint arXiv:1412.1897},
  year={2014}
}

For more information regarding the paper, please visit www.evolvingai.org/fooling

Requirements

This is an installation process that requires two main software packages (included in this package):

1. Caffe: http://caffe.berkeleyvision.org

• Our libraries installed to work with Caffe
  • Cuda 6.0
  • Boost 1.52
  • g++ 4.6

2. Sferes: https://github.com/jbmouret/sferes2

• Our libraries installed to work with Sferes
  • OpenCV 2.4.10
  • Boost 1.52
  • g++ 4.9 (a C++ compiler compatible with C++11 standard)

Note: These are specific versions of the two frameworks with our additional work necessary to produce the images as in the paper. They are not the same as their master branches.

Installation

Please see the Installation_Guide.pdf for more details.

Usage

• An MNIST experiment (Fig. 4, 5 in the paper) can be run directly on a local machine (4-core) within a reasonable amount of time (around ~5 minutes or less for 200 generations).
• An ImageNet experiment needs to be run on a cluster environment. It took us ~4 days x 128 cores to run 5000 generations and produce 1000 images (Fig. 8 in the paper).
• How to configure an experiment to test the evolutionary framework quickly

License

Please refer to the licenses of Sferes and Caffe projects.