Create a new model and train it to be robust to Fast Gradient Method attack

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ephi...@yahoo.com

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Nov 27, 2018, 6:37:12 PM11/27/18
to cleverhans dev
What does it mean to "Create a new model and train it to be robust to Fast Gradient Method attack." It appears that the "model2" is trained and evaluated with adversarial examples generated when updating clean/legitimate input x  with values of adv_x. Is that all there is to it? For the MNIST case, for example, how does an initial 9-10% test accuracy on adversarial images become >95% test accuracy after retraining? Is there some other "make robust" algorithm implemented?
Best, AT 

Ian Goodfellow

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Nov 27, 2018, 6:44:09 PM11/27/18
to ephi...@yahoo.com, cleverhans dev
This part is just referring to training on FGSM adversarial examples. Training the model on FGSM adversarial examples makes it become robust to them.

On Tue, Nov 27, 2018 at 3:37 PM 'ephi...@yahoo.com' via cleverhans dev <cleverh...@googlegroups.com> wrote:
What does it mean to "Create a new model and train it to be robust to Fast Gradient Method attack." It appears that the "model2" is trained and evaluated with adversarial examples generated when updating clean/legitimate input x  with values of adv_x. Is that all there is to it? For the MNIST case, for example, how does an initial 9-10% test accuracy on adversarial images become >95% test accuracy after retraining? Is there some other "make robust" algorithm implemented?
Best, AT 

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ephi...@yahoo.com

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Nov 27, 2018, 7:07:38 PM11/27/18
to cleverhans dev
Thank you. 


On Tuesday, November 27, 2018 at 6:44:09 PM UTC-5, Ian Goodfellow wrote:
This part is just referring to training on FGSM adversarial examples. Training the model on FGSM adversarial examples makes it become robust to them.

On Tue, Nov 27, 2018 at 3:37 PM 'ephi...@yahoo.com' via cleverhans dev <cleverhans-dev@googlegroups.com> wrote:
What does it mean to "Create a new model and train it to be robust to Fast Gradient Method attack." It appears that the "model2" is trained and evaluated with adversarial examples generated when updating clean/legitimate input x  with values of adv_x. Is that all there is to it? For the MNIST case, for example, how does an initial 9-10% test accuracy on adversarial images become >95% test accuracy after retraining? Is there some other "make robust" algorithm implemented?
Best, AT 

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