Irritating Image-Based AIs with Style
This Project achieved the 4th place in 2018's Informaticup and recieved additional honour for the best scientific elaboration.
The goal was to produce false positives for a street-sign detecting neural network. We accomplished it with 3 different approaches, found in the root-folders of this repository. For the quickest intro to this topic, refer to the Präsentation_gi.
The documentation is done in german language only, except for the abstract. The code is done in english.
The Degeneration is found in the folder Degeneration. It contains an elaborate notebook with additional commentary and conception insights, and it`s readable from your browser. We therefore recommend to first dive in here. The Degeneration.py holds the same code as a common python file. Additional python files are used for several sub-tasks, such as imagealternation and remote-communication.
Saliency Maps contains a jupyter-notebook which performs every required task. The used methods for saliency-generation are found in the subfolder aux_functions and contain core-logic of this approach. The subfolder data contains some sample input-images and some sample output for visualisation - so the user doesn`t need to search for the gtsrb-dataset.
GradientAscent contains the training of the AlexNet and performs the gradient-ascent method for every of the 43 classes. In the folder data there are some example images.
The folder Latex in root contains our scientific work and the required tex-files to compile it. The only other notable instance there bib-file, which contains all our used sources and further reading.
The presentations are found in the root-folder, the Präsentation_GI is a slimmed version for the informaticup-jury, while the Präsentation_TH was for the university (and is a little longer). Both are in german.
All sub-projects are build with python in Jupyter-Notebooks. The used python version was 3.6 and several anaconda-packages are required.
The exact required packages are denoted (and downloaded) by the saliency-maps and gradient ascent in the notebooks themselves, for degeneration it`s put in the scientific work chapter 3.1 table 3.1, which also summarizes all required technologies in detail.
To rebuild Aphrodite and the AlexNet you will require the GTRSB data-set.
For further instructions on training aphrodite see the trainingfile.
To run the Degeneration you will need a real street-sign in size 64x64, we therefore recommend using the test-data of the gtsrb-set. Some images are provided in the degeneration-subfolder images Images from the training-set performed badly, probably due to overfitting of the remote-ai.
To run the saliency map the whole gtsrb-dataset is used.
Please don`t. After our final presentation this repository will be archived.
However it`s completely open to you, so feel free to fork it and reach out to us.
We are a group of 4 IT-Master-students at the TH Nuremberg Georg Simon Ohm. We participated in the competition as part of our it-project.
- Leonhard Applis
- Peter Bauer
- Andreas Porada
- Florian Stöckl
Where Leonhard is also the repo-owner, if you want to blame him for anything here.