A Psycholinguistics-inspired Method to Counter IP Theft Using Fake Documents

Abstract

Intellectual property (IP) theft is a growing problem. We build on prior work to deter IP theft by generating n fake versions of a technical document so a thief has to expend time and effort in identifying the correct document. Our new SbFAKE framework proposes, for the first time, a novel combination of language processing, optimization, and the psycholinguistic concept of surprisal to generate a set of such fakes. We start by combining psycholinguistic-based surprisal scores and optimization to generate two bilevel surprisal optimization problems (an Explicit one and a simpler Implicit one) whose solutions correspond directly to the desired set of fakes. As bilevel problems are usually hard to solve, we then show that these two bilevel surprisal optimization problems can each be reduced to equivalent surprisal-based linear programs. We performed detailed parameter tuning experiments and identified the best parameters for each of these algorithms. We then tested these two variants of SbFAKE (with their best parameter settings) against the best performing prior work in the field. Our experiments show that SbFAKE is able to more effectively generate convincing fakes than past work. In addition, we show that replacing words in an original document with words having similar surprisal scores generates greater levels of deception.