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Citation overkill

I removed a slew of references from the first sentence because it's not a controversial statement. This broke some references by name, which some bot will surely come and fix in a short while. More generally, I think this article has too many references per statement, and could do with some trimming so that only the best/most cited (pick any one) remain. QVVERTYVS (hm?) 09:23, 15 January 2015 (UTC)[reply]

The first sentence makes it clear that this article is about the security applications of adversarial machine learning, not adversarial machine learning itself. This article should be linked to by a topic exclusively on adversarial machine learning. Adversarial machine learning tunes it's learning to precise learning rather than average learning. It optimizes relative to a minimax value of the game approach. I've only read one story about adversarial machine learning, but this article does not tell me anything about it outside of security applications. I am not an expert on this at all, but adversarial machine learning has many applications beyond security. Dave44000 (talk) 12:08, 17 October 2016 (UTC)[reply]

My wording edits

I am not knowledgeable in this field, but I just made a few small edits that--I hope--clear up some confusions. I'm still left with many parts of this article that are hard to follow, and where I don't feel confident enough to make a change. For example, if Google mangled a picture of a dog so *both* humans and computer vision systems mis-classified it, what does that have to do with adversarial machine learning? Sounds more like significant image distortion. How does denial of service "increase the wrong classification rate"? (taxonomy section) What is "Snort"? (referred to in the "attacks against clustering algorithms" section) "If clustering can be safely adopted in such settings, this remains questionable": what does "this" refer to? (same section) What is a "ladder algorithm" or a "Kaggle-style competition"? Here hyperlinks, or at least references to outside discussion, are needed. — Preceding unsigned comment added by Mcswell (talk • contribs) 04:30, 1 June 2019 (UTC)[reply]

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Expanding the definition of Adversarial Machine Learning

This wiki-document is a nice summary of the main referenced paper, but the definitions are incorrect. Adversarial is a generic term that does not need to apply to only the context of malicious behaviours. In the context of machine learning, adversarial can mean competition between learning systems in order to accelerate and augment the learning process. This is exactly the case for Generative Adversarial Networks. I realize the article makes some claims regarding the term; nevertheless, applying the term in such a narrow way is incorrect.

Alternatively, adversarial machine learning applies to the more general idea of coordinating the results of multiple systems that have conflicting goals in order to train one or all of the systems in some optimized way. With this definition, we can categorize the work of self-play game play intelligence as adversarial machine learning.

Here is an example: https://subscription.packtpub.com/book/game_development/9781789138139/5/ch05lvl1sec38/adversarial-self-play

The article should be expanded to take into account other adversarial machine learning ideas and the definition changed to update this.

Bruce.matichuk (talk) 18:55, 25 June 2019 (UTC)Bruce.matichuk[reply]

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