{"data":{"id":"03584865-f6a6-4ba2-8f7e-b4bdfa98950c","title":"FGRW: Fine-Grained Reversible Watermarking Based on Distribution-Adaptive Contrastive Augmentation Across Diverse Domains","summary":"The paper proposes FGRW, a fine-grained reversible watermarking scheme for protecting ownership of pre-trained models after cross-domain fine-tuning. It generates trigger and task samples using out-of-distribution awareness, and a representation-oriented regularization constraint selects modification spaces while the embedding loss guides the augmentation network. The authors report superior performance in fine-grained authorization and reversible integrity verification, and identify a trade-off between training a clean ideal model and obtaining a watermarked model.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11197919","publishedAt":"2025-10-09T13:17:21.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":null,"fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}