2 ms·
The issue is that NNs are continuous, by design. The more linear and continuous they are, the easier they are to train and generalize well. But what that means
by Houshalter 11y ago
The issue is that NNs are continuous, by design. The more linear and continuous they are, the easier they are to train and generalize well.
But what that means is, changing each input by a small amount also changes the output by a small amount. And images have lots of pixels. Changing each of them a tiny amount in the right direction adds up to a big change to the output.
- p4wnc6 11y agoI see what you mean now. This plus the comment from user `argonaut` helped me realize what I was missing. Not all of generalization error is because of overfitting, and even if none of it is because of overfitting, the idea you describe would still allow for the attack.