{"data":{"id":"3bad60a3-fae1-4048-a1c2-b413dea9bed0","title":"QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition","summary":"This paper proposes QCLR, a plug-in robustification module for contrastive learning in self-supervised skeleton-based human action recognition. It applies a dual-level quantization scheme across the encoding pipeline, which breaks gradient propagation and absorbs small adversarial perturbations within quantization bins. Experiments on benchmark datasets show improved robustness against gradient-based white-box and gradient-free black-box attacks while keeping classification accuracy highly competitive.","solution":"The proposed mitigation is the QCLR module itself, which integrates a dual-level quantization scheme and an attention-guided multi-faceted quantization strategy into contrastive learning models to improve adversarial robustness.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2610.13281v1","publishedAt":"2026-10-09T09:30:15.000Z","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":"2026-10-09T09:30:15.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"preprint","atlasIds":null}}