{"data":{"id":"69be2281-f9da-4dd9-a01b-8dc2ad569703","title":"Boosting Adversarial Training With Mitigating Hard Sample Interference","summary":"This paper proposes mitigating hard sample interference (MHSI), a method for adversarial training that reduces the accuracy-robustness trade-off caused by samples near the decision boundary. It combines a weighted adaptive (WA) mechanism that strengthens learning on clean samples with a dynamic calibration (DC) strategy that adjusts hard-sample probability outputs, guided by gradient norm and Hessian analysis. Experiments on CIFAR-10, CIFAR-100, Tiny ImageNet and SVHN report up to a 6.22% robustness gain over the AT baseline under an l-infinity attack.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11329528","publishedAt":"2026-01-06T13:16:36.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"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":"2026-01-06T13:16:36.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}