This will be really interesting when people start attacking the visual networks of autonomous cars. That giant poster at the side of the road that looks like an ad for toothpaste, but that all autonmous cars seem to slow down for...

Maybe the happy path of current autonomous cars isn't that they are tested in Arizona or Californa with traction and sunshine- but that they aren't being attacked by adversarial input. (I do remember that guy that painted a line on the ground that trapped cars though).

What will it mean if we suddenly realize that convolutional neural network object recognition is too easily fooled to be a secure part of autonomous vehicles? Would that push the state of the art backwards a long way, or would it not matter because there are other alternatives?

> convolutional neural network object recognition

I find it really strange how a neural network, which is supposed to be a set of elements operating in the continuous domain, is so vulnerable to what looks like small-amplitude noise.

The autonomous cars could check some some official database of road signs and report if something is off and even put the strange sign in captchas for humans to check it :)

> even put the strange sign in captchas for humans to check it

"Please select all pictures that look like a legitimate street sign. Please hurry."

Audio Adversarial Examples http://nicholas.carlini.com/code/audio_adversarial_examples/

I remember there was an advertisement on TV where an actor would say "okay google, what is X?", and the phones nearby would search for that phrase. With this, any advertisement could be made to activate nearby phones to search for any phrase or do anything really, without the ad itself containing any google-related phrases.

Not yet.

The audio adversarial examples we construct in this paper do not remain adversarial after being played over-the-air, and therefore present a limited real-world threat; however, just as the initial work on image-based adversarial examples did not consider the physical channel and only later was it shown to be possible, we believe further work will be able to produce audio adversarial examples that are effective over-the-air.

Weird, I'd expect that since they've achieved a 100% success rate, they could get at least 50% in real life scenarios. Also this could be played in stores to get the phones of customers to search for certain phrases.

Their 100% success rate requires perfect fidelity. If they're saying it doesn't work over the air, they probably have close to 0% success on store speakers.

Right, it looks very similar to the early adversarial visual examples. At first they only worked on direct simulated inputs with perfect clarity, then images, and now live camera feed of arbitrary 3D printed objects from any angle.

Can't the technique itself be used to better train the speech recognition systems?

Using the first example where it sounds like "without the dataset the article is useless" but the speech recognition thinks it hears "okay google browse to evil dot com"; you could use that to train the recognition system to recognize it correctly as what humans think they heard.

Of course, many attacks would need to be used to create lots of training data.

I'm not ready to call BS on this, but I'm deeply skeptical of the general language they're using. They might have iteratively gotten particular recognition to do what they say, but I don't think they've gotten recognition in general.

I plugged the first 4 examples at http://nicholas.carlini.com/code/audio_adversarial_examples/ into Google Docs' own Voice typing, and got:

1: At the yard course Eustis

2: Set the Artic course Eustis

3: (nothing, it's an operatic wall of sound)

4: (nothing, it's an operatic wall of sound)

Given that Nick is one of the world's experts on adversarial examples against NN, and given that he already has another paper attacking voice recognition systems, i'd hold off before calling this work "BS"

Does it require perfect knowledge of the object?

Not sure what you mean by "object", but this is a white-box attack against Mozilla's DeepSpeech model that relies on being able to compute gradients for the complete pipeline. They didn't test whether the adversarial examples transfer to other models.

Ooops, my model was flooded with beer when I typed it. Indeed I meant model...

From the abstract: Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (at a rate of up to 50 characters per second).

That sentence is quite easy to misunderstand. The 50 characters per second are the capacity of the attacked model (it chops the audio in frames of 1/50th second), and what they want to highlight is that they can cram the maximum amount of text into the adversarial example.

The actual generation takes an hour on an nVidia 1080Ti, but they can parallelize the process to compute multiple examples on the same GPU, giving an amortized cost of a few minutes.