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The number of papers submitted to academic conferences is steadily rising inmany scientific disciplines. To handle this growth, systems for automaticpaper-reviewer assignments are increasingly used during the reviewing process.These systems use statistical topic models to characterize the content ofsubmissions and automate the assignment to reviewers. In this paper, we showthat this automation can be manipulated using adversarial learning. We proposean attack that adapts a given paper so that it misleads the assignment andselects its own reviewers. Our attack is based on a novel optimization strategythat alternates between the feature space and problem space to realizeunobtrusive changes to the paper. To evaluate the feasibility of our attack, wesimulate the paper-reviewer assignment of an actual security conference (IEEES&P) with 165 reviewers on the program committee. Our results show that we cansuccessfully select and remove reviewers without access to the assignmentsystem. Moreover, we demonstrate that the manipulated papers remain plausibleand are often indistinguishable from benign submissions.

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning