This week I'm at CVPR — the IEEE's Computer Vision and Pattern Recognition Conference, which is a huge AI event. I'm currently rehearsing the timing of my talk one last time, but I wanted to take a minute between run-throughs to link to my co-author Steven Forsyth's wonderful post on the NVIDIA research blog about our paper.

Steven does a fantastic job of describing our work, so head over there to see what he has to say. I couldn't resist putting a post of my own because (a) I love this video we created...

...and (b), Steven left out what I think was the most convincing result we had, which shows that BaRT achieves a Top-1 accuracy on ImageNet that is higher than the Top-5 accuracy of the previous state-of-the-art defense, Adversarial Training.

Also, (c) I am very proud of this work. It's been an idea I've been batting around for almost three years now, and I finally got approval from my client to pursue it last year. It turns out it works exactly how I expected, and I can honestly say that this is the first — and probably only — time in my scientific career that has ever happened.

If you want a copy of the paper, complete with some code in the appendices, ((Our hands are somewhat tied releasing the full code due to the nature of our client relationship with the wonderful Laboratory for Physical Sciences, who funded this work.)) our poster, and the slides for our oral presentation you can find it on the BaRT page I slapped together on my website.