paper-with-me

Papers

On the Detection of Reviewer-Author Collusion Rings From Paper Bidding

2024-02-12 · Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu

A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipulate the conference's paper assignment, with the aim of being assigned to review each other's papers. The most straightforward way that colluding reviewers can manipulate the paper assignment is by indicating their interest in each other's papers through strategic paper bidding. One potential approach to solve this important problem would be to detect the colluding reviewers from their manipulated bids, after which the conference can take appropriate action. While prior work has developed effective techniques to detect other kinds of fraud, no research has yet established that detecting collusion rings is even possible. In this work, we tackle the question of whether it is feasible to detect collusion rings from the paper bidding. To answer this question, we conduct empirical analysis of two realistic conference bidding datasets, including evaluations of existing algorithms for fraud detection in other applications. We find that collusion rings can achieve considerable success at manipulating the paper assignment while remaining hidden from detection: for example, in one dataset, undetected colluders are able to achieve assignment to up to 30% of the papers authored by other colluders. In addition, when 10 colluders bid on all of each other's papers, no detection algorithm outputs a group of reviewers with more than 31% overlap with the true colluders. These results suggest that collusion cannot be effectively detected from the bidding using popular existing tools, demonstrating the need to develop more complex detection algorithms as well as those that leverage additional metadata (e.g., reviewer-paper text-similarity scores).

📄 PDF Abstract BibTeX arXiv:2402.07860

Code (1)

sjecmen/peer-review-collusion-detection 공식 구현

Tasks

Fraud Detectiontext similarity

Similar Papers 제목 키워드 기반

Combating Collusion Rings is Hard but Possible

2021-12-14 · Niclas Boehmer, Robert Bredereck, André Nichterlein

A recent report of Littmann [Commun. ACM '21] outlines the existence and the fatal impact of collusion rings in academic peer reviewing. We introduce and analyze the problem Cycle-Free Reviewing that aims at finding a re…

Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions

2024-12-09 · Jhih-Yi, Hsieh, aditi raghunathan, Nihar B. Shah

In the peer review process of top-tier machine learning (ML) and artificial intelligence (AI) conferences, reviewers are assigned to papers through automated methods. These assignment algorithms consider two main factors…

Text Matchingtext similarity

CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review

2026-09-04 · Jicheng Zhou, Kemou Li, Kahim Wong, Zheyuan Li 외 arxiv

Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciprocal assignment advantage. Prior work treats bidding, reviewer assignment, and review manipulation as separate st…

Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models

2026-06-11 · Jianwei Fei, Yunshu Dai, Zhihua Xia, Xiaochun Cao 외 arxiv

Model fingerprinting, embedding user-specific identifiers (fingerprints) into generated outputs, has recently emerged as a popular solution to protect the intellectual property rights (IPR) of generative text-to-image (T…

Image Generation

Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards

2025-05-08 · Jaeho Kim, Yunseok Lee, Seulki Lee

The peer review process in major artificial intelligence (AI) conferences faces unprecedented challenges with the surge of paper submissions (exceeding 10,000 submissions per venue), accompanied by growing concerns over …

Position