Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics. Preserving the anonymity of a text's writer can be also important in some other contexts, e.g., in the case of witness protection or anonymity programs. However, each person has his her own style of writing, which can be analyzed using stylometry, and as a result, the true identity of the author of a piece of text can be revealed even if s he has tried to hide it. Thus, it could be helpful to design automatic tools that can help a person obfuscate his her identity when writing text. In particular, here we propose an approach that changes the text, so that it is pushed towards average values for some general stylometric characteristics, thus making the use of these characteristics less discriminative. The approach consists of three main steps: first, we calculate the values for some popular stylometric metrics that can indicate authorship; then we apply various transformations to the text, so that these metrics are adjusted towards the average level, while preserving the semantics and the soundness of the text; and finally, we add random noise. This approach turned out to be very efficient, and yielded the best performance on the Author Obfuscation task at the PAN-2016 competition.
@cite_9 explored author masking by detecting the most commonly-used words by the target author and then trying to change them. They also mention the application of machine translation as a possible approach to author obfuscation. Other authors also used machine translation for author obfuscation @cite_14 @cite_6 , e.g., by translating passages of text from English to one or more other languages and then back to English. @cite_14 investigated three different approaches to adversarial stylometry: obfuscation (masking author style), imitation (trying to copy another author's style), and machine translation. They further summarized the most common features people used to obfuscate their own writing style. @cite_10 developed a complex system for author obfuscation which consists of three main modules: canonization (unifying case, normalizing white spaces, spelling correction, etc.), event set determination (extraction of events significant for author detection, such as words, parts of speech bi- or tri-grams, etc.), and statistical inference (measures that determine the results and confidence in the final report). The authors used this same approach @cite_21 to detect deliberate style obfuscation.
@cite_14 @cite_9 @cite_21 @cite_6 @cite_10
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The use of stylometry, authorship recognition through purely linguistic means, has contributed to literary, historical, and criminal investigation breakthroughs. Existing stylometry research assumes that authors have not attempted to disguise their linguistic writing style. We challenge this basic assumption of existing stylometry methodologies and present a new area of research: adversarial stylometry. Adversaries have a devastating effect on the robustness of existing classification methods. Our work presents a framework for creating adversarial passages including obfuscation, where a subject attempts to hide her identity, and imitation, where a subject attempts to frame another subject by imitating his writing style, and translation where original passages are obfuscated with machine translation services. This research demonstrates that manual circumvention methods work very well while automated translation methods are not effective. The obfuscation method reduces the techniques' effectiveness to the level of random guessing and the imitation attempts succeed up to 67p of the time depending on the stylometry technique used. These results are more significant given the fact that experimental subjects were unfamiliar with stylometry, were not professional writers, and spent little time on the attacks. This article also contributes to the field by using human subjects to empirically validate the claim of high accuracy for four current techniques (without adversaries). We have also compiled and released two corpora of adversarial stylometry texts to promote research in this field with a total of 57 unique authors. We argue that this field is important to a multidisciplinary approach to privacy, security, and anonymity. This paper explores techniques for reducing the effectiveness of standard authorship attribution techniques so that an author A can preserve anonymity for a particular document D. We discuss feature selection and adjustment and show how this information can be fed back to the author to create a new document D' for which the calculated attribution moves away from A. Since it can be labor intensive to adjust the document in this fashion, we attempt to quantify the amount of effort required to produce the anonymized document and introduce two levels of anonymization: shallow and deep. In our test set, we show that shallow anonymization can be achieved by making 14 changes per 1000 words to reduce the likelihood of identifying A as the author by an average of more than 83 . For deep anonymization, we adapt the unmasking work of Koppel and Schler to provide feedback that allows the author to choose the level of anonymization. Whistleblowers and activists need the ability to communicate without disclosing their identity, as of course do kidnappers and terrorists. Recent advances in the technology of stylometry (the study of authorial style) or "authorship attribution" have made it possible to identify the author with high reliability in a non-confrontational setting. In a confrontational setting, where the author is deliberately masking their identity (i.e. attempting to deceive), the results are much less promising. In this paper, we show that although the specific author may not be identifiable, the intent to deceive and to hide his identity can be. We show this by a reanalysis of the Brennan and Greenstadt (2009) deception corpus and discuss some of the implications of this surprising finding. Authorship attribution is an important and emerging security tool. However, just as criminals may wear gloves to hide their fingerprints, so too may criminal authors mask their writing styles to escape detection. Most authorship studies have focused on cooperative and or unaware authors who do not take such precautions. This paper analyzes the methods implemented in the Java Graphical Authorship Attribution Program (JGAAP) against essays in the Brennan-Greenstadt obfuscation corpus that were written in deliberate attempts to mask style. The results demonstrate that many of the more robust and accurate methods implemented in JGAAP are effective in the presence of active deception.