{"data":{"id":"dcde4cbc-370a-43b8-88f2-2489ae587fd8","title":"Adversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound","summary":"Researchers evaluated two black-box adversarial attacks against a U-Net model that segments thyroid nodules in B-mode ultrasound images, trained on cine-clips from 192 nodules. The spatial-domain attack SSAA reduced mean DSC from 0.76 to 0.47 while keeping SSIM at 0.94, and the frequency-domain attack FDUA reduced it to 0.65 (SSIM 0.82). Three inference-time defenses significantly recovered DSC against SSAA, with deterministic denoising giving the largest gain (+0.10), but none significantly improved results against FDUA.","solution":"The three inference-time mitigations evaluated were randomized preprocessing with test-time augmentation, deterministic input denoising, and stochastic ensemble inference with consistency-aware aggregation. They partially mitigated spatial-domain perturbations, but statistically significant DSC recovery was not demonstrated against frequency-domain perturbations.","labels":["security","research"],"sourceUrl":"https://doi.org/10.1371/journal.pdig.0001797","publishedAt":"2026-11-04T00:00:00.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["U-Net"],"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":"2026-11-04T00:00:00.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}