[argonaut]

Are you referring to this foveation paper (http://arxiv.org/abs/1511.06292)? I'm quite skeptical of the claims in that paper; upon closer reading their experiments are problematic. Also, it appears the paper was rejected. I can elaborate if that is indeed the case.

[ta7552]

> I can elaborate if that is indeed the case.

Please do anyway.

I'm wondering whether adversarial examples can also be found for autoencoders to the same extent. It seems very intuitive that you can overstep the decision boundary that a discriminatory network learns by slightly shifting the input into the direction of a different, nearby label.

[aab0]

Yes. And rejection means little. The point is that adversarial examples have to be fragiley constructed to fool on one single example for one forward-pass. There is no evidence that any adversarial examples exist which can fool an even slightly more sophisticated CNN, fool a simple CNN over many time-steps, fool a simple CNN for enough time-steps to lead to any noticeable differences in action, fool a simple CNN for enough time-steps to lead to a noticeable difference in action which could lead to an accident, or fool a simple CNN for enough time-steps to lead to a noticeable difference in action which leads to an accident frequently enough to noticeably reduce the safety advantages.

[argonaut]

The paper was rejected (you can read the ICLR comments) because the experiments did not really support their point. And I agree. The gist of the experiments they ran to support their thesis was to take a CNN and construct adversarial examples that sucessfully fooled it. They then applied foveation, and showed that the CNN was no longer fooled. Which is obvious! It's kind of obvious to me that adding preprocessing that the attacker is unaware of would be able to beat the attacker. What they didn't do is regenerate the adversarial examples assuming the attacker has knowledge that the target was using foveation.

There are no experiments that support your statements, unfortunately.