PDF Mirage: Content masking attack against information-based online services

Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


We present a new class of content masking attacks against the Adobe PDF standard, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We show three attack variants with notable impact on real-world systems. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our final attack, we place masked content into the indexes for Bing, Yahoo!, and DuckDuckGo which renders as information entirely different from the keywords used to locate it, enabling spam, profane, or possibly illegal content to go unnoticed by these search engines but still returned in unrelated search results. Lastly, as these systems eschew optical character recognition (OCR) for its overhead, we offer a comprehensive and lightweight alternative mitigation method.

Original languageEnglish
Title of host publicationProceedings of the 26th USENIX Security Symposium
PublisherUSENIX Association
Number of pages15
ISBN (Electronic)9781931971409
StatePublished - 2017
Event26th USENIX Security Symposium - Vancouver, Canada
Duration: Aug 16 2017Aug 18 2017

Publication series

NameProceedings of the 26th USENIX Security Symposium


Conference26th USENIX Security Symposium


Dive into the research topics of 'PDF Mirage: Content masking attack against information-based online services'. Together they form a unique fingerprint.

Cite this