{"data":{"id":"3cb47906-5fab-4fa2-b830-23fc92100458","title":"MAPLE: manifold-aligned purification with logit-edge calibration for robust black-box adversarial defense","summary":"MAPLE is a black-box adversarial defense framework that pairs a fully convolutional Semantic Purification Network (SPN) with a Margin Calibration Classifier (MCC) initialized from the target classifier. The two components are trained through cyclic co-calibration to remove adversarial perturbations while enlarging the local classification margin. The paper reports that MAPLE consistently outperforms previous mainstream methods under diverse black-box attacks on CIFAR-10 and Mini-ImageNet.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://doi.org/10.1007/s40747-026-02538-3","publishedAt":"2026-10-12T00:00:00.000Z","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":"2026-10-12T00:00:00.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}