{"data":{"id":"0181f554-b225-41cb-8b6b-581d32205ca4","title":"A Study of the Removability of Speaker-Adversarial Perturbations","summary":"This research studies whether adversarial perturbations (small, intentional noise added to audio that tricks speaker recognition systems into misidentifying who is speaking) can be removed from speech. The study tested three scenarios based on how much information a defense system has about the attack: knowing nothing about it, having partial information, and having complete information. The results showed that removing these perturbations is only possible when the defense system has full knowledge of how the attack was generated, while partial or no knowledge makes complete removal difficult or impossible.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11517498","publishedAt":"2026-05-13T13:17:56.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-05-13T13:17:56.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}