{"data":{"id":"8555980f-e308-4c01-be3b-b206cc8c1359","title":"Adversarial Purification by Consistency-Aware Latent Space Optimization on Data Manifolds","summary":"This paper proposes Consistency Model-based Adversarial Purification (CMAP), which removes adversarial perturbations from inputs to deep neural networks by optimizing vectors in the latent space of a pre-trained consistency model. The method combines a perceptual consistency restoration mechanism, a latent distribution consistency constraint, and an ensemble-based latent vector consistency prediction scheme. Experiments on CIFAR-10 and ImageNet-100 report significantly improved robustness against strong adversarial attacks while preserving high natural accuracy.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11655486","publishedAt":"2026-08-14T13:16:20.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","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":"2026-08-14T13:16:20.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.95,"researchCategory":"peer_reviewed","atlasIds":null}}