Writing abstracts and introductions for my research papers used to be a bit of a dark art for me, where I would just sort of vaguely summarize the main thrust of my paper and hope that it got the point across. Lately, though, after reading enough abstracts I think I know what the “formula” sort of is, and I thought it would be a good idea to write a post about it. So here we are.
The most helpful advice for writing abstracts I’ve ever read came from this blog post by Nicholas Carlini, and I highly recommend reading through the whole thing because it is chock full of good advice. Here, though, I’ll just focus on writing abstracts.
I think one thing that I’ve realized is that it is perfectly okay for abstracts and introductions to be a bit fomulaic. In fact if you follow the zeitgesit established by other papers in your field, it will be easier for people familiar with that schema to understand your paper and there is a higher chance it will be accepted. I think in general, scientific writing is a bit more rigid than fiction for this very reason. It is important for there to be no surprises in the structure of your paper so that the content (which is the important part!) comes through as cleanly as possible.
https://ieeexplore.ieee.org/abstract/document/7958570
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x’ that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks’ ability to find adversarial examples from 95% to 0.5%. In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples.