{"data":{"id":"03584865-f6a6-4ba2-8f7e-b4bdfa98950c","title":"FGRW: Fine-Grained Reversible Watermarking Based on Distribution-Adaptive Contrastive Augmentation Across Diverse Domains","summary":"This paper describes a new watermarking technique (a method to embed hidden ownership markers into AI models) that remains stable when models are fine-tuned (adjusted to perform new tasks) across different domains. The researchers propose a system that automatically adjusts synthetic training samples and watermark embedding based on the specific data, using out-of-distribution awareness (detecting when data differs significantly from expected patterns) to keep the watermark robust while maintaining the model's performance on its actual task.","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,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}