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Brick shattering through window hypothesis finally has evidence No seriously - am I missing something? "Recent research has revealed that the output of Deep N

• rlayton2 9y ago
  My guess here is that we need to learn more about adding noise to datasets before they go into a neural network. Lots of existing work do alterations like transposing, rotating, flipping and so on. Adding noise, including known attacks, would be part of this.
  • goldenkey 9y ago
    Noise will definitely help if random noise is added to every input image before the neural network sees it, in production. Essentially, a pre-input pipeline state that a bad actor cannot predict, making single pixel deception totally neutered. I think what needs to be done here is to add a threshold of correlation between the input pixels. Consider that the problem is that 1 pixel change in the deviously right way, can be equivalent to the change when multiple pixels are changed in the proper way -- the derivative of the cost function. So clearly there needs to be a way to design / tell the network that 1 pixel change cannot be nearly as strong as changing multiple pixels relationships, in terms of cost function value change. From what I can garner, the only way to accomplish this is to make sure the number of nodes in the hidden layers is strictly monotonically decreasing. By using the last layer as a "grab bag" for classification, with 100s of nodes greater than the previous layers, the network becomes vulnerable to single pixel attacks. There have to be ways to design classification styles networks without the fan-out.