Metadata-Version: 2.1
Name: adversarial-friend
Version: 1.1.8
Summary: Group Project for CS 5523
Home-page: https://github.umn.edu/LI002252/CS_5523_Final_Project.git
Author: Adversarial_Friends Group
Author-email: li002252@umn.edu
Maintainer:  Mario Serrafero, Jacob Isler 
Maintainer-email: isle0011@umn.edu, serra082@umn.edu
License: MIT
Description: 
        # CS_5523_Final_Project
        
        **This repo contains code and files of final project in CSCI 5523: Data Mining**
        
        ## Getting Started
        
        To import AdversarialFriend API, use the following pip command: `pip install adversarial-friend`
        
        ## Team Members
        
        Yangyang Li <li002252@umn.edu>
        
        Jacob Isler <isle0011@umn.edu>  
        
        Mario Serrafero <serra082@umn.edu>
        
        ## Project Abtract
        
        We will explore parameter interpretation techniques for computer vision tasks, in order to get a deeper understanding of how to deal with real image data. Specifically speaking, the models to be used include generalized linear models like Logistic Regression, as well as more powerful techniques such as convolutional neural networks. What is more, we will document and implement these algorithms to extract the learned concepts that our models detect in input images. Finally, we will use the information, along with automatic input optimization, to programmatically generate adversarial examples using model-agnostic gradient-based methods that trick the learned models into misclassifying input images.
        
        For full project report, go to: [add link])()
        
        ## Reference
        
        [1] C. Olah, A. Satyanarayan, I, Johnson, S. Carter, L. Schubert, K. Ye, A. Mordvinstev, “The Building Blocks of Interpretability” Distill, 6-Mar-2018. [Online](https://distill.pub/2018/building-blocks/) [Accessed: 1-Dec-2020]
        
        [2] J. Johnson, EECS 498-007 / 598-005 Deep Learning for Computer Vision, University of Michigan, 10-Aug-2020. [Online](https://www.youtube.com/watch?v=qcSEP17uKKY&t=823s) [Accessed: 1-Dec-2020]
        
        [3]  A. Kurakin, I. Goodfellow, S. Bengio, “Adversarial Examples in the Physical World” ICLR, 8-Jul-2016. [Online](https://arxiv.org/pdf/1607.02533.pdf) [Accessed: 1-Dec-2020]
        
        ## License
        
        This is free and unencumbered software released into the public domain.
        
        Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
