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The abstract for the 2019 journal article entitled ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X'' published in ''Proceedings on Privacy Enhancing Technologies'' indicates:

The abstract for the 2019 journal article entitled ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X'' published in ''Proceedings on Privacy Enhancing Technologies'' indicates:

{{Cquote|...a genetic algorithm based random search framework called Mutant-X which can automatically obfuscate text to successfully evade attribution while keeping the semantics of the obfuscated text similar to the original text. Specifically, Mutant-X sequentially makes changes in the text using mutation and crossover techniques while being guided by a fitness function that takes into account both attribution probability and semantic relevance. While Mutant-X requires black-box knowledge of the adversary’s classifier, it does not require any additional training data and also works on documents of any length. We evaluate Mutant-X against a variety of authorship attribution methods on two different text corpora. Our results show that Mutant-X can decrease the accuracy of state-of-the-art authorship attribution methods by as much as 64% while preserving the semantics much better than existing automated authorship obfuscation approaches.<ref>[https://content.sciendo.com/downloadpdf/journals/popets/2019/4/article-p54.pdf ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X''] by Asad Mahmood, Faizan Ahmad, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar, ''Proceedings on Privacy Enhancing Technologies''; 2019 (4):54–71</ref>}}

{{Cquote|...a genetic algorithm based random search framework called Mutant-X which can automatically obfuscate text to successfully evade attribution while keeping the semantics of the obfuscated text similar to the original text. Specifically, Mutant-X sequentially makes changes in the text using mutation and crossover techniques while being guided by a fitness function that takes into account both attribution probability and semantic relevance. While Mutant-X requires black-box knowledge of the adversary’s classifier, it does not require any additional training data and also works on documents of any length. We evaluate Mutant-X against a variety of authorship attribution methods on two different text corpora. Our results show that Mutant-X can decrease the accuracy of state-of-the-art authorship attribution methods by as much as 64% while preserving the semantics much better than existing automated authorship obfuscation approaches.<ref>[https://content.sciendo.com/downloadpdf/journals/popets/2019/4/article-p54.pdf ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X''] by Asad Mahmood, Faizan Ahmad, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar, ''Proceedings on Privacy Enhancing Technologies''; 2019 (4):54–71</ref>}}

== See also ==

== See also ==