60 lines
4.0 KiB
Markdown
60 lines
4.0 KiB
Markdown
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# Fooling Code
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This is the code base used to reproduce the "fooling" images in the paper:
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[Nguyen A](http://anhnguyen.me), [Yosinski J](http://yosinski.com/), [Clune J](http://jeffclune.com). ["Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"](http://arxiv.org/abs/1412.1897). In Computer Vision and Pattern Recognition (CVPR '15), IEEE, 2015.
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**If you use this software in an academic article, please cite:**
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@inproceedings{nguyen2015deep,
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title={Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images},
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author={Nguyen, Anh and Yosinski, Jason and Clune, Jeff},
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booktitle={Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on},
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year={2015},
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organization={IEEE}
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}
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For more information regarding the paper, please visit www.evolvingai.org/fooling
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## Requirements
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This is an installation process that requires two main software packages (included in this package):
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1. Caffe: http://caffe.berkeleyvision.org
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* Our libraries installed to work with Caffe
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* Cuda 6.0
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* Boost 1.52
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* g++ 4.6
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* Use the provided scripts to download the correct version of Caffe for your experiments.
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* `./download_caffe_evolutionary_algorithm.sh` Caffe version for EA experiments
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* `./download_caffe_gradient_ascent.sh` Caffe version for gradient ascent experiments
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2. Sferes: https://github.com/jbmouret/sferes2
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* Our libraries installed to work with Sferes
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* OpenCV 2.4.10
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* Boost 1.52
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* g++ 4.9 (a C++ compiler compatible with C++11 standard)
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* Use the provided script `./download_sferes.sh` to download the correct version of Sferes.
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Note: These are patched 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.
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## Installation
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Please see the [Installation_Guide](https://github.com/anguyen8/opencv_contrib/blob/master/modules/dnns_easily_fooled/Installation_Guide.pdf) for more details.
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## Usage
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* 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).
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* 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).
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* [How to configure an experiment to test the evolutionary framework quickly](https://github.com/Evolving-AI-Lab/fooling/wiki/How-to-test-the-evolutionary-framework-quickly)
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* To reproduce the gradient ascent fooling images (Figures 13, S3, S4, S5, S6, and S7 from the paper), see the [documentation in the caffe/ascent directory](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/caffe/ascent). You'll need to download the correct Caffe version for this experiment using `./download_caffe_gradient_ascent.sh` script.
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## Troubleshooting
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1. If Sferes (Waf) can't find your CUDA and Caffe dynamic libraries
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> Add obj.libpath to the wscript for exp/images to find libcudart and libcaffe or you can use LD_LIBRARY_PATH (for Linux).
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2. Is there a way to monitor the progress of the experiments?
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> There is a flag for printing out results (fitness + images) every N generations.
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You can adjust the dump_period setting [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images.cpp#L159).
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3. Where do I get the pre-trained Caffe models?
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> For AlexNet, please download on Caffe's Model Zoo.
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> For LeNet, you can grab it [here](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/model/lenet).
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4. How do I run the experiments on my local machine without MPI?
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> You can enable MPI or non-MPI mode by commenting/uncommenting a line [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images_mnist.cpp#L190-L191). It can be simple eval::Eval (single-core), eval::Mpi (distributed for clusters).
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