Metadata-Version: 2.1
Name: moorkh
Version: 0.0.2
Summary: Buddhu is a Adversarial examples generation library
Home-page: https://github.com/akshay-gupta123/moorkh
Author: Akshay Gupta
Author-email: akshay.bis2000@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/akshay-gupta123/moorkh/issues
Description: # moorkh : Adversarial Attacks in Pytorch
        
        **moorkh** is a Pytorch library for generating adversarial examples with full support for batches of images in all attacks.
        
        ## About the name
        
        The name moorkh is a Hindi word meaning Fool in English, that's what we are making to Neural networks by generating advesarial examples. Although we also do so for making them more robust.
        
        ## Usage
        
        ### Installation
        
        - `pip install moorkh` or
        - `git clone https://github.com/akshay-gupta123/moorkh`
        
        ```python
        import moorkh
        norm_layer = moorkh.Normalize(mean,std)
        model = nn.Sequential(
            norm_layer,
            model
        )
        model.eval()
        attak = moorkh.FGSM(model)
        adversarial_images = attack(images, labels)
        ```
        ## Implemented Attacks
        
        * **[`EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES: FGSM`](https://arxiv.org/abs/1412.6572)**
        * **[`ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD: IFGSM`](https://arxiv.org/abs/1607.02533)**
        * **[`ON THE LIMITATION OF CONVULATIONSAL NEURAL NETWORK IN RECOGNIZING NEGATIVE IMAGES: Semantic`](https://arxiv.org/abs/1703.06857)**
        * **[`ADDING NOISE: Noise`](https://arxiv.org/abs/1802.00420)**
        * **[`TOWARDS DEEP LEARNING MODELS RESISTANT TO ADVERSARIAL ATTACKS: PGD\L2`](https://arxiv.org/abs/1706.06083)**
        * **[`ESEMBLE ADVERSAIAL TRAINING: ATTACKS and DEFENSE: RFGSM`](https://arxiv.org/abs/1705.07204)**
        
        ## To-Do's
          * Adding more Attacks
          * Making Documentation
          * Adding demo notebooks
          * Adding Summaries of Implemented papers(for my own undestanding)
        
        ## Contribution
        
        This library is developed as a part of my learning, if you find any **bug** feel free to create a PR. All kind of contributions are always welcome! 
        
        ##  References
        
        * [Adversarial=Robustness-Toolbox](https://github.com/IBM/adversarial-robustness-toolbox) by IBM.
        * [Foolbox](https://github.com/bethgelab/foolbox) by Bethgelab.
        * [Cleverhans](https://github.com/tensorflow/cleverhans) by Google brain
        * [Reliable and Interpretable Artificial Intelligence](https://www.sri.inf.ethz.ch/teaching/riai2020) A Eth Zurich course
        * [Adversarial Robustness - Theory and Practice](https://adversarial-ml-tutorial.org/) Tutorial by Zico Kolter and Aleksander Madry
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
