Content Moderation Classifiers Under Adversarial Rephrasing: A Benchmark Study

Authors

  • Costanza Panzarella Centre for Language Technology, Ciociaria Centre for Machine Intelligence, Frosinone, Italy Author
  • Domagoj Blazevic-Sim Department of Information Security, Baranja Centre for Machine Intelligence, Beli Manastir, Croatia Author
  • Miloslav Rejsek Centre for Responsible AI, Slovacko Centre for Machine Intelligence, Uherske Hradiste, Czech Republic Author

DOI:

https://doi.org/10.5281/zenodo.22660889

Keywords:

Content Moderation Classifiers Under Adversarial Rephrasing; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Content Moderation Classifiers Under Adversarial Rephrasing is an area of active development in applied machine learning, with
evidence that has accumulated unevenly across its component questions. We review the current evidence base for content
moderation classifiers under adversarial rephrasing, spanning methodological, implementation, governance, and cost dimensions,
focusing on where practice has converged and where it continues to vary, and set out the practical considerations relevant to a
team considering its adoption. We conclude that selective, context-specific use is best supported by the evidence currently
available, rather than uniform adoption or avoidance.

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Emmerich T, Tarasov E, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a comparative analysis". Proc ICLR 24(2014): 388-395. DOI: 10.2196/69678

Foscarini C, Chiriac F, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a field deployment report". ACM Trans Intell Syst 26(2017): 399-407. DOI: 10.7759/cureus.46454

Alvez D, Dumitrescu A, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a benchmark study". Data Min Knowl Discov 28(2020): 410-419. DOI: 10.1016/j.ssci.2025.107028

Brankovic K, Kowalczyk B, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.2196/48633

Castellani R, Ristic C, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.1371/journal.pone.0305949

Dumitrescu A, Andric D, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.1371/journal.pdig.0000514

Eriksdottir B, Brankovic E, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.17219/acem/208596

Farkas I, Ionescu F, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.7759/cureus.104086

Grabowski P, Petrescu G, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.1038/s41746-023-00852-5

Hendriksen W, Wojcik H, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.1016/j.mayocpiqo.2022.09.004

Ionescu F, Foscarini I, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.1177/08404704241291226

Jankowiak G, Grabowski J, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.1093/medlaw/fwad013

Kowalczyk N, Novotna K, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.2196/89834

Lindqvist U, Urbanek L, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.1007/s10916-024-02104-9

Marchetti D, Doncheva M, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.3389/fdgth.2026.1896184

Novotna E, Eriksdottir N, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.3390/diagnostics15162117

Ostrowski L, Lindqvist O, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.4103/jrpp.jrpp_20_65

Petrescu S, Szabo P, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.3389/fdgth.2026.1763884

Quintero B, Balogh Q, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a case study". Proc FAccT 20(2020): 586-593. DOI: 10.2196/48297

Ristic C, Castellani R, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a survey". Nat Mach Intell 22(2023): 597-605. DOI: 10.1007/s00330-025-12031-z

Szabo J, Jankowiak S, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a comparative analysis". J Mach Learn Res 24(2014): 608-617. DOI: 10.3389/fdgth.2026.1699005

Tarasov Q, Quintero T, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a field deployment report". IEEE Trans Pattern Anal Mach Intell 26(2017): 619-629. DOI: 10.3389/fdgth.2026.1834800

Urbanek Z, Zielinski U, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a benchmark study". Proc NeurIPS 28(2020): 630-641. DOI: 10.2196/69678

Vasquez A, Alvez V, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a systematic evaluation". Proc ICML 30(2023): 641-653. DOI: 10.7759/cureus.46454

Wojcik H, Hendriksen W, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.1016/j.ssci.2025.107028

Zielinski O, Ostrowski Z, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.2196/48633

Andric V, Vasquez A, et al. "Content Moderation Classifiers Under Adversarial Rephrasing: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.1371/journal.pone.0305949

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Published

2025-09-13

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Vol. 4 No. 2 (2025): vol 4 issue 2 2025

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