{"data":{"id":"f9d7b1c2-65ae-475b-8b7f-de2f1d9dc517","title":"Armor: Shielding Unlearnable Examples Against Data Augmentation","summary":"Researchers show that data augmentation, a standard pre-processing step, can undo the protection of unlearnable examples, raising the accuracy of a model trained on protected data from 21.3% to 66.1%. They propose Armor, a defense framework that uses a non-local module-assisted surrogate model, a surrogate augmentation selection strategy and dynamic step size adjustment to preserve unlearnability under augmentation. Across 4 datasets and 5 augmentation methods, Armor reduces test accuracy on augmented protected samples by as much as 60% more than six baseline defenses.","solution":"Armor is the proposed defense: it adds defensive noise to samples, using a surrogate model and augmentation selection to keep protected data unlearnable under data augmentation. The authors state they will open-source their code upon publication.","labels":["privacy","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11345171","publishedAt":"2026-01-12T13:22:15.000Z","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":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}