{"data":{"id":"f16ae6e1-e193-4d84-945c-99a93f22f03f","title":"Adversary-Robust Graph-Based Learning of WSIs","summary":"The authors propose a graph-based defense model for whole slide images (WSIs) used in cancer diagnosis. It combines a graph neural network, a denoising module based on untrained neural network prior and graph signal processing, and a vision transformer for prostate cancer Gleason grading. Tested against various adversarial attacks, the model showed a significant improvement in diagnosis accuracy.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11495509","publishedAt":"2026-04-28T13:17:23.000Z","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":"2026-04-28T13:17:23.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}