Tutorial For One Pixel Attack

[mehulrastogi]

Noting down the list of tasks to be completed for the tutorial. The implementation for the attack is in #65 .

Paper Link

For the sake of simplicity right now focusing on one model and one dataset. (VGG16 , CIFAR10(less noisy than kaggle cifar10) )

• [ ] #79
• [ ] #78
• [ ] Targeted attack on random 300 samples (can be a separate tutorial)

[mehulrastogi]

The following can be used to get useful stats for the tutorial `

    attack_image = obj.perturbation_image(attack_result.x, image)
    prior_probs = obj.model_predict(image)
    predicted_probs = self.model_predict(attack_image)
    predicted_class = np.argmax(predicted_probs)
    actual_class = original_label
    success = predicted_class != actual_class
    cdiff = prior_probs[actual_class] - predicted_probs[actual_class]

`

[Shreyas-Bhat]

Hey, I would like to work on this

[someshsingh22]

great @Shreyas-Bhat you can take it up, comment on #78 and #79 too so we can assign it to you