recently I am working on model compression and if it influenced model robustness while facing adversarial samples, I tried ex06.py on cifar10, and after generating adversarial data, it would test against adversarial test data:
print('Testing against adversarial test data')
score = model.evaluate(X_adv, y_test)
print('\nloss: {0:.4f} acc: {1:.4f}'.format(score[0], score[1]))
I am a little confused, higher accuracy means better or worse robustness? or it's not related?
so could you please explain a little about adversarial data, thx!
recently I am working on model compression and if it influenced model robustness while facing adversarial samples, I tried ex06.py on cifar10, and after generating adversarial data, it would test against adversarial test data: print('Testing against adversarial test data') score = model.evaluate(X_adv, y_test) print('\nloss: {0:.4f} acc: {1:.4f}'.format(score[0], score[1])) I am a little confused, higher accuracy means better or worse robustness? or it's not related? so could you please explain a little about adversarial data, thx!