That's a good idea, though note that there's a difference between asking "Which of these two images looks more like a panda?" and "Which of these two images looks more like a panda than a dog or cat?". The latter is the…
> Agreed. That is surprising, and also increases the security risks, because I can produce images on my in-house network and then take them out into the world to fool other networks without even having access to the…
> ...in case 2 I can compute the gradient numerically, it just takes a bit longer. Yep, true, might just take a while. On the other hand, even a very noisy estimate of the gradient might suffice, which could be faster…
No, but we did make use of (1) a large number of input -> network -> output iterations, along with (2) precisely measured output values to decide which input to try next. It may not be so easy to experiment in the same…
> This work has led to some unfortunate misconceptions. Agreed; the weaknesses reported should definitely not be taken to affect only convnets or only deep learning. Ian's "Explaining and Harnessing Adversarial…
That's a good idea, though note that there's a difference between asking "Which of these two images looks more like a panda?" and "Which of these two images looks more like a panda than a dog or cat?". The latter is the…
> Agreed. That is surprising, and also increases the security risks, because I can produce images on my in-house network and then take them out into the world to fool other networks without even having access to the…
> ...in case 2 I can compute the gradient numerically, it just takes a bit longer. Yep, true, might just take a while. On the other hand, even a very noisy estimate of the gradient might suffice, which could be faster…
No, but we did make use of (1) a large number of input -> network -> output iterations, along with (2) precisely measured output values to decide which input to try next. It may not be so easy to experiment in the same…
> This work has led to some unfortunate misconceptions. Agreed; the weaknesses reported should definitely not be taken to affect only convnets or only deep learning. Ian's "Explaining and Harnessing Adversarial…