No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
This exposes vulnerabilities in automated review systems used by academic conferences, posing a threat to the integrity of peer review processes.
The authors tackled the problem of manipulating automatic paper-reviewer assignment systems by developing an adversarial attack that adapts papers to mislead assignments and select reviewers, achieving success in simulations with 165 reviewers at an IEEE S&P conference while keeping papers plausible.
The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems use statistical topic models to characterize the content of submissions and automate the assignment to reviewers. In this paper, we show that this automation can be manipulated using adversarial learning. We propose an attack that adapts a given paper so that it misleads the assignment and selects its own reviewers. Our attack is based on a novel optimization strategy that alternates between the feature space and problem space to realize unobtrusive changes to the paper. To evaluate the feasibility of our attack, we simulate the paper-reviewer assignment of an actual security conference (IEEE S&P) with 165 reviewers on the program committee. Our results show that we can successfully select and remove reviewers without access to the assignment system. Moreover, we demonstrate that the manipulated papers remain plausible and are often indistinguishable from benign submissions.