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Computer Vision with Python: adversarial attack using cleverhans module

Deliverable: Working python code, preferably using Jupyter Notebook and showing images modified using adversarial attack methods.

I have a road sign classifier model that works perfectly with the GTSRB datase (download: [login to view URL])

My next step is to fool this classifier. It has a very high accuracy (up to 99%) when classifying "clean" images.

I want to test 1 or 2 types of adversarial attacks to the model and see how much the accuracy decreases. There is a python module called [login to view URL] so there is no need to start coding these adversarial algorithms from scratch. But I don't have time to study this module.

I am thinking to test either saliency map attack or Carlini-Wagner:

from [login to view URL] import SaliencyMapMethod

from [login to view URL] import CarliniWagnerL2

I'd also like to compare "clean" images versus the adversarial images.

Some examples I found online using a different dataset are:

[login to view URL]

[login to view URL]

See the attachment for jupyter notebook code of classifying GTSRB dataset with high accuracy

Skills: Python, Machine Learning (ML), Computer Vision, Neural Networks, Artificial Intelligence

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About the Employer:
( 5 reviews ) ISTANBUL, Turkey

Project ID: #30096822

9 freelancers are bidding on average $12/hour for this job

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