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Samudi Amarasinghe

Publications and source records attributed to Samudi Amarasinghe.

2 recordsLinked to original sources

BVI: Lightweight, Data-Centric Blockchain-Based Verification of Identity Claims

A person who answers an unexpected call claiming to come from a bank has no way to check the claim. Australian text messaging has labelled a message as unverified when the sender identifier is not registered since 1 July 2026, but a voice call still arrives with nothing behind it, and voice cloning has removed the last cue recipients relied on. We present BVI, which answers one question for the recipient: did the calling party prove it holds a credential a registered organisation issued for this call? BVI keeps the organisational record, its authorised channels and its revocation state on a public ledger in a directly queryable form, and puts all decision logic in the handset, which performs eleven checks, pays no transaction fee and holds no full-chain state. We define ten attack classes plus the case in which revocation cannot be resolved, compare them in a 10-by-4 coverage matrix spanning BBCA, the content digest, the detector and composed BVI, and run 27 automated scenario tests against isolated Hardhat fixture states of the BVI contract; all 27 pass. For media substitution and synthetic speech, the contract tests validate commitment and gating properties rather than claiming detector accuracy. The mandatory per-session anchor costs about 91.5k gas and is invariant in call duration and hop count; each participating intermediary additionally contributes one optional attestation write. In a 20,000-session-per-cell path simulation, at one quarter carrier participation BVI detects 20.6 per cent of randomly located in-path rewrites versus 0.1 per cent for the every-hop comparison, while identity and content evidence remain available even when path evidence is indeterminate. We also quantify the throughput ceiling of the anchoring design.

cs.CR↗

D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection

Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult to verify. Existing systems usually rely on a single evidence source. Content-based detectors identify local inconsistencies within an image and its caption but cannot determine global factual truth. Retrieval-based fact-checkers reason over external evidence but treat inputs as coarse claims and often miss subtle visual or textual manipulations. This separation creates failure cases where internally consistent fabrications bypass manipulation detectors and fact-checkers verify claims that contain pixel-level or token-level corruption. We present D-SECURE, a framework that combines internal manipulation detection with external evidence-based reasoning for news-style posts. D-SECURE integrates the HAMMER manipulation detector with the DEFAME retrieval pipeline. DEFAME performs broad verification, and HAMMER analyses residual or uncertain cases that may contain fine-grained edits. Experiments on DGM4 and ClaimReview samples highlight the complementary strengths of both systems and motivate their fusion. We provide a unified, explainable report that incorporates manipulation cues and external evidence.

cs.CV↗