(98%) Deqiang Li Shicheng Cui Yun Li Jia Xu Fu Xiao Shouhuai Xu ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms. PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks. This data I'd be happy to hear from you what it was. Simplistic (but reasonably well calibrated)īag-of-words classifier believes the given paper The new un-verified entries will have a probability indicated that my
Then that I'll remove the ones that aren't related toįalse positives on the most recent few entries. I do this filtering roughly twice a week, and it's Get a chance to manually filter through them. This list automatically updates with new papers, even before I Send me an email if something is wrong and I'll correct it.Īs a result, this list is completely un-filtered.Įverything that mainly presents itself as an adversarialĮxample paper is listed here I pass no judgement of quality.įor a curated list of papers that I think are excellent andĪdversarial Machine Learning Reading List. Judgement calls as to whether or not any given paper is These criteria (and are about something different instead), I also may have included papers that don't match Or extensively uses adversarial examples.ĭue to the sheer quantity of papers, I can't guarantee
Is that it is primarily a paper about adversarial examples, The only requirement I used for selecting papers for this list Papers for the last few years, and realized it may be helpful I have been somewhat religiously keeping track of these Where we have seen massive growth in the number of papers It can be hard to stay up-to-date on the published papers in
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