{"data":{"id":"16c4de0e-4c4d-496f-b17f-fb45cbbba4de","title":"Anchored Rank-Adversarial Fine-Tuning of Pretrained Visual Place Recognition Models","summary":"Foundation-model visual place recognition models such as SuperVLAD and BoQ fall to under 0.2% top-1 recall under a white-box ranking attack at a 1/255 budget. The authors propose anchored rank-adversarial fine-tuning (ARAFT), which tunes a released model with a listwise rank loss and an anchor to the frozen model's clean descriptors. ARAFT raises robust R@1 to 47.3 on SuperVLAD and 55.3 on BoQ within a fixed clean-accuracy budget, where plain adversarial training yields no admissible checkpoint.","solution":"ARAFT: fine-tune a released VPR model with a listwise rank loss on each adversarial query's hardest positive, plus an anchor to the clean descriptor of the frozen pretrained model, selecting checkpoints under a predefined clean-accuracy budget.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2611.05286v2","publishedAt":"2026-11-03T18:01:18.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["SuperVLAD","BoQ"],"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":"2026-11-03T18:01:18.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"preprint","atlasIds":null}}