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Detecting Hoaxes, Frauds and Deception in Writing Style Online
Sadia Afroz, Michael Brennan and Rachel GreenstadtPrivacy, Security and Automation Lab
Drexel University
Sunday, October 21, 12
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What do we mean by “deception?”
Let me give an example…
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A Gay Girl In Damascus
A blog byAmina Arraf
A Syrian-American activistLives in Damascus
Facts about Amina:
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Sunday, October 21, 12
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Sunday, October 21, 12
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Sunday, October 21, 12
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Sunday, October 21, 12
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Sunday, October 21, 12
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Sunday, October 21, 12
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A Gay Girl In Damascus
Sunday, October 21, 12
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Fake picture (copied from Facebook)
A Gay Girl In Damascus
Sunday, October 21, 12
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Thomas MacMasterA 40-year old American male
Fake picture (copied from Facebook)
The real “Amina”
A Gay Girl In Damascus
Sunday, October 21, 12
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Why we are interested?
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Why we are interested?
Thomas developed a new writing style for Amina
Sunday, October 21, 12
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Why we are interested?
Thomas developed a new writing style for Amina
One member of alternate-history Yahoo! group wrote:
“If you read through her blog entries, its pretty clear its our Amina. Same background, same interests, same style of writing.
I can confirm she's the same.”
Sunday, October 21, 12
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• Deception in Writing Style:–Someone is hiding his regular writing style
• Research question:–If someone is hiding his regular style, can we detect it?
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Why do we care?• Security:–To detect fake internet identities, astroturfing, and hoaxes
• Privacy and anonymity:–To understand how to anonymize writing style
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• How to detect authorship of a document?
• Can we circumvent authorship recognition?
• Can we detect if someone is trying to circumvent authorship recognition?
• How to anonymize writing style?
Overview
Sunday, October 21, 12
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• How to detect authorship of a document?
• Can we circumvent authorship recognition?
• Can we detect if someone is trying to circumvent authorship recognition?
• How to anonymize writing style?
Overview
Sunday, October 21, 12
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Authorship recognition
Who wrote the document? Can be determined using writing style
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Does everybody have unique writing style?
• Most people do!• Because everybody learns language
differently
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WHAT IS THIS OBJECT?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?… a sofa?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?… a sofa?… a davenport?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?… a sofa?… a davenport?… a chesterfield?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?… a sofa?… a davenport?… a chesterfield?… a divan?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?… a sofa?… a davenport?… a chesterfield?… a divan?… a settee?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHAT IS THIS OBJECT?Is this a couch?… a sofa?… a davenport?… a chesterfield?… a divan?… a settee?Regional differences
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
• “on the left of”?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
• “on the left of”?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
• “on the left of”?
• “at the plate’s left”?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
• “on the left of”?
• “at the plate’s left”?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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WHERE IS THE DINNER FORK?
• “next to” the plate?
• “to the left of”?
• “on the left of”?
• “at the plate’s left”?
• “left of” the plate?Thanks to Patrick Juola for this example
Sunday, October 21, 12
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FUNCTION WORDS
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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FUNCTION WORDS
FINISHED FILES ARE NOT THE RESULTOF YEARS OF SCIENTIFIC STUDY
COMBINED WITH THE EXPERIENCE OFMANY YEARS.
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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FUNCTION WORDS
• How many times does the letter ‘F’ appear in this passage?
FINISHED FILES ARE NOT THE RESULTOF YEARS OF SCIENTIFIC STUDY
COMBINED WITH THE EXPERIENCE OFMANY YEARS.
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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FUNCTION WORDS
• How many times does the letter ‘F’ appear in this passage?
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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FUNCTION WORDS
• How many times does the letter ‘F’ appear in this passage?
• Many people (most?) only count three
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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FUNCTION WORDS
• How many times does the letter ‘F’ appear in this passage?
• Many people (most?) only count three• They miss the word ‘OF.’
Thanks to Patrick Juola for this exampleSunday, October 21, 12
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Authorship Recognition• Modern authorship recognition systems are
machine learning based.• Supervised• Unsupervised
Sunday, October 21, 12
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How good are current authorship recognition algorithms?
• 100 authors (Writeprints: A Stylometric Approach to Identity-Level
Identification and Similarity Detection in Cyberspace. Abbasi et al.)• 10,000 authors (content-based approach)
(“Authorship attribution in the wild, ” Koppel et al.)• 100,000 authors (“On the Feasibility of Internet-Scale Author
Identification,” Narayanan et al.)
Sunday, October 21, 12
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Threat Scenario: Alice the Anonymous Blogger vs. Bob the
Abusive Employer. Alice blogs about abuses at Bob’s company.
Blog posted anonymously (Tor, pseudonym, etc). Bob obtains 5000-10000 words of each employee’s
writing. Bob uses authorship recognition to identify Alice as the blogger.
Sunday, October 21, 12
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• How to detect authorship of a document?
• Can we circumvent authorship recognition?
• Can we detect if someone is trying to circumvent authorship recognition?
• How to anonymize writing style?
Overview
Sunday, October 21, 12
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Assumption of Authorship recognition
•Writing style is invariant.
–It’s like a fingerprint, you can’t really change it.
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Wrong Assumption!
•Imitation or framing attack–Where one author imitates another author•Obfuscation attack–Where an author hides his regular style
M. Brennan and R. Greenstadt. Practical attacks against authorship recognition techniques. In Proceedings of the Twenty-First Conference on Innovative Applications of
Artificial Intelligence (IAAI), Pasadena, CA, 2009.
Sunday, October 21, 12
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Imitating Cormac McCarthy
“On the far side of the river valley the road passed through a stark black burn. Charred and limbless trunks of trees stretching away on every side. Ash moving over the road and the sagging hands of b l ind wi re s t rung f rom the blackened lightpoles whining thinly in the wind.”
Sunday, October 21, 12
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Your goal is to fool the computer into thinking that your passage was NOT written by you.
You may use whatever means you wish so long as the writing would not raise any eyebrows when a human reads over it (no scrambled words, mixed up semantics, etc) and the point is still clearly conveyed.
Obfuscating writing style
Sunday, October 21, 12
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Sunday, October 21, 12
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Sunday, October 21, 12
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Sunday, October 21, 12
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• How to detect authorship of a document?
• Can we circumvent authorship recognition?
• Can we detect if someone is trying to circumvent authorship recognition?
• How to anonymize writing style?
Overview
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Can we detect stylistic deception?
Deceptive Regular
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Can we detect stylistic deception?
Deceptive Regular
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Analytic Approach
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Analytic Approach
Data Collection
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Analytic Approach
Data Collection Feature Extraction
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Analytic Approach
Data Collection ClassificationFeature Extraction
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Analytic Approach
Data Collection ClassificationFeature Extraction
Feature Ranking
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Data collection
• Short-term deception: • Long-term deception:
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Data collection
• Short-term deception:– Extended-Brennan-
Greenstadt Corpus• Fixed topic• Controlled style
• Long-term deception:
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Data collection
• Short-term deception:– Extended-Brennan-
Greenstadt Corpus• Fixed topic• Controlled style
– Hemingway-Faulkner Imitation corpus• No fixed topic• Controlled style
• Long-term deception:
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Data collection
• Short-term deception:– Extended-Brennan-
Greenstadt Corpus• Fixed topic• Controlled style
– Hemingway-Faulkner Imitation corpus• No fixed topic• Controlled style
• Long-term deception:-Thomas-Amina Hoax corpus• No fixed topic• No control in style
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• Participants– 12 Drexel students– 56 AMT authors
Extended-Brennan-Greenstadt Corpus
• Writing samples– Regular (5000-word)– Imitation (500-word)
– Imitate Cormac McCarthy–Topic: A day
– Obfuscation (500-word)–Write in a way they don’t
usually write–Topic: Neighborhood
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• Classification task:• Distinguish Regular, Imitation and
Obfuscation
Extended-Brennan-Greenstadt Corpus
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Classification• We used WEKA for machine learning.
• Classifier: –Experimented with several classifiers–Choose the best classifier for a feature set
• 10-fold cross-validation–90% of data used for training–10% of data used for testing
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• We experimented with 3 feature sets:–Writeprints–Lying-detection features–9-features
Feature sets
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• We experimented with 3 feature sets:–Writeprints• 700+ features, SVM• Includes features like frequencies of word/character n-
grams, parts-of-speech n-grams.–Lying-detection features–9-features
Feature sets
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• We experimented with 3 feature sets:–Writeprints• 700+ features, SVM
–Lying-detection features• 20 features, J48 decision tree• Previously used for detecting lying.• Includes features like rate of Adjectives and Adverbs,
sentence complexity, frequency of self-reference. –9-features
Feature sets
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• We experimented with 3 feature sets:–Writeprints
• 700+ features, SVM–Lying-detection features
• 20 features, J48 decision tree–9-features• 9 features, J48 decision tree• Used for authorship recognition• Includes features like readability index, number of
characters, average syllables.
Feature sets
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How the classifier uses changed and unchanged features
•We measured–How important a feature is to the classifier (using information gain ratio)–How much it is changed by the deceptive users
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How the classifier uses changed and unchanged features
• We measured–How important a feature is to the classifier (using information gain ratio)–How much it is changed by the deceptive users
• We found–For words, characters and parts-of-speech n-grams information gain increased as features were changed more. –The opposite is true for function words (of, for, the)
•Deception detection works because deceptive users changed n-grams but not function words.
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Problem with the dataset:Topic Similarity
• All the adversarial documents were of same topic.•Non-content-specific features have same
effect as content-specific features.
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Hemingway-Faulkner Imitation Corpus
Faux Faulkner Contest International Imitation Hemingway Competition
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• Participants– 33 contest winners
• Writing samples– Regular• Excerpts of Hemingway• Excerpts of Faulkner
– Imitation• Imitation of Hemingway• Imitation of Faulkner
Hemingway-Faulkner Imitation Corpus
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• Classification task:• Distinguish Regular and Imitation
Hemingway-Faulkner Imitation Corpus
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Imitation successAuthor to imitate Imitation
successWriter’s Skill
Cormac McCarthy
47.05% Not professional
Ernest Hemingway
84.21% Professional
William Faulkner 66.67% Professional
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Long term deception
• Writing samples– Regular• Thomas’s writing sample at
alternate-history Yahoo! group– Deceptive• Amina’s writing sample atalternate-history Yahoo! group• Blog posts from “A Gay Girl in
Damascus”
• Participant– 1 (Thomas)
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Long term deception
• Classification:• Train on short-term deception corpus• Test blog posts to find deception
• Result:• 14% of the blog posts were deceptive (less
than random chance).
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Long term deception:Authorship Recognition
•We performed authorship recognition of the Yahoo! group posts.
•None of the Yahoo! group posts written as Amina were attributed to Thomas.
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•We tested authorship recognition on the blog posts. • Training:– writing samples of Thomas (as himself), – writing samples of Thomas (as Amina), – writing samples of Britta (Another suspect of this
hoax).
Long term deception:Authorship Recognition
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Thomas MacMaster (as himself): 54%Thomas MacMaster (as Amina Arraf): 43%Britta: 3%
Long term deception:Authorship Recognition
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Thomas MacMaster (as himself): 54%Thomas MacMaster (as Amina Arraf): 43%Britta: 3%
Maintaining separate writing styles is hard!
Long term deception:Authorship Recognition
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• How to detect authorship of a document?
• Can we circumvent authorship recognition?
• Can we detect if someone is trying to circumvent authorship recognition?
• How to anonymize writing style?
Overview
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Why not machine translation?
They passed through the city at noon of the day following.
(German) (Japanese)
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Why not machine translation?
They passed through the city at noon of the day following.
(German) (Japanese)
They passed the city at noon the following day.
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Why not machine translation?
Just remember that the things you put into your head are there forever, he said.
(German)
(Japanese)
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Why not machine translation?
Just remember that the things you put into your head are there forever, he said.
(German)
(Japanese)
You are dead, that there always is set, please do not forget what he said.
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Why not machine translation?
Machine translation does not anonymize writing style because:– A good translator does not change the style that
much– A bad translator completely changes the
meaning
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How about imitation?• Task: Change a pre-existing document by
imitating Cormac McCarthy
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I can't pinpoint the exact moment I started to break.
The girl sitting in the pristine and serene and sterile psychiatrist office couldn’t pinpoint the moment she started breaking.
After Imitation
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How to anonymize writing style? JStylo
! ! ! ! ! Authorship Recognition Tool (Lead developer: Ariel Stolerman)
Anonymouth Authorship Recognition Circumvention Tool (Lead developer: Andrew McDonald)
Alpha release available: https://psal.cs.drexel.eduSunday, October 21, 12
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Anonymouth user study• 10 participants– 6500+ pre-existing documents– 500-word document to modify
• Background corpus: 6 authors documents• Classifier: 9-features and SVM
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Limitations • On an extensive feature set, Anonymouth
gives suggestions like:– Use fewer instances of the letter “I”Hard for users to follow
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• How to detect authorship of a document?– Using writing style
• Can we circumvent authorship recognition?– Yes! By imitating or obfuscating.
• Can we detect if someone is trying to circumvent authorship recognition?– Yes! Using a large feature set. But hard to detect long-
term style change.• How to anonymize writing style?
– Anonymouth (https://psal.cs.drexel.edu)
Summary
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Thank you!
• Sadia Afroz: [email protected]• Michael Brennan: [email protected]• Ariel Stolerman: [email protected]• Andrew McDonald: [email protected]• Aylin Caliskan: [email protected]• Rachel Greenstadt: [email protected]
• Privacy, Security And Automation Lab (https://psal.cs.drexel.edu)
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