2 ms·

Adversarial examples are tied to a specific algorithm, they didn't produce any universal adversarial examples from what I understand.

• tormeh 12y ago
  If I understood it correctly, they looked at the activation of the individual high-level neurons and edited the image such that the high-level activation values changed a lot while the image was still identical to the human eye.
• streptomycin 12y ago
  From the article: What’s more surprising is that the same perturbation can cause a different network, which was trained on a different training dataset, to misclassify the same image. It means that adversarial examples are somewhat universal.
  • cpa 12y ago
    Indeed, but they change the dataset and keep the same algorithm. I didn't see anything about changing the algorithm in itself. That's not very surprising: if you have enough training data, any large training set will be typical because it'll be average enough. But when you choose a specific algorithm, you can exploit its weaknesses and throwing more data won't change anything. Change the algorithm will, though.
    • streptomycin 12y ago
      But why would different training data lead to the same error? I could imagine it would lead to something with the same type of flaw, but why do the same exact adversarial images work, out of the near infinite number of possible images? Doesn't intuitively make sense to me, but I can't say I have much of a background in machine learning. Like if you fit 5 close-to-linear 2d points with a 4th order polynomial, you'll overfit. Change the data slightly and you'll still overfit, but your fit will be very different.