Towards Fine-grained and Robust Detection of Human-AI Co-authorship

Summary

The authors formulate a discrete-class text provenance framework that sorts human-AI co-authorship into distinct degrees of AI involvement, separating surface-level AI polishing from AI restructuring of discourse. They show that adversarial paraphrasing substantially degrades detectors trained only on benign data. Augmenting training data with adversarial paraphrases produces consistent robustness gains and good cross-generator transferability.

Mitigation

Augment detector training data with adversarial paraphrases generated by the state-of-the-art paraphrasing attack model.

Related items

• InfoAnchored Rank-Adversarial Fine-Tuning Pretrained Visual Place Recognition ModelsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoSay the Same, Act Differently: Diagnosing and Steering Text-Action Divergence in Reasoning VLAsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoDetect, Repair and Audit: A Hierarchical Multi-Agent Defense Against Adversarial Attacks in Energy ForecastingSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoSANAI: Secure ECC Distributed Keyed Parity Embedded in Weights for Protecting Neural Networks against Bit-Flip AttacksSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoVulnerabilities and Defenses in Audio-Visual Attacks: A Survey From Audio to Multimodal ModelsSimilar attack · ACM Digital Library (TOPS, DTRAP, CSUR)