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_site/js/publications_data.js

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@@ -19,7 +19,7 @@ const PUBLICATIONS_DATA_LOCAL = [
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"conf": "https://sites.google.com/view/mv2026/task-description"
2020
},
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"img": "/img/Publications/2026_ICMR_Shahid.png",
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"abstract": ""
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"abstract": "Verifying social media content from active conflict zones requires rapid geolocation, source attribution, forensic analysis, and multi-platform verification—tasks that overwhelm individual analysts at scale. We present Multi-agent OSINT Swarm for Automated Information Verification (MOSAIV), a three-stage agentic swarm built on Large Language Models (LLMs) for automated verification of multimedia news. MOSAIV operates in three sequential phases: (1) a Prime Agent use 50 labeled training samples via few-shot in-context learning to produce a shared context document and a reusable 7-step verification skill specification; (2) multiple parallel Verification Agents each process social media posts using the primed skill, performing live web searches, OSINT analysis, and structured report generation; and (3) a dedicated Localization Agent independently verifies GPS coordinates and produces bounding-box-annotated evidence images, dual-panel OpenStreetMap location cards, and live source evidence thumbnails, multiple evidence artifacts per run in total. We evaluated 10 conflict-zone validation cases provided by the MV2026 challenge."
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},
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{
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"title": "HIDE: Detecting Diffusion-Based Inpainting via Latent h-Space Representation",

js/publications_data.js

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -19,7 +19,7 @@ const PUBLICATIONS_DATA_LOCAL = [
1919
"conf": "https://sites.google.com/view/mv2026/task-description"
2020
},
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"img": "/img/Publications/2026_ICMR_Shahid.png",
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"abstract": ""
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"abstract": "Verifying social media content from active conflict zones requires rapid geolocation, source attribution, forensic analysis, and multi-platform verification—tasks that overwhelm individual analysts at scale. We present Multi-agent OSINT Swarm for Automated Information Verification (MOSAIV), a three-stage agentic swarm built on Large Language Models (LLMs) for automated verification of multimedia news. MOSAIV operates in three sequential phases: (1) a Prime Agent use 50 labeled training samples via few-shot in-context learning to produce a shared context document and a reusable 7-step verification skill specification; (2) multiple parallel Verification Agents each process social media posts using the primed skill, performing live web searches, OSINT analysis, and structured report generation; and (3) a dedicated Localization Agent independently verifies GPS coordinates and produces bounding-box-annotated evidence images, dual-panel OpenStreetMap location cards, and live source evidence thumbnails, multiple evidence artifacts per run in total. We evaluated 10 conflict-zone validation cases provided by the MV2026 challenge."
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},
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{
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"title": "HIDE: Detecting Diffusion-Based Inpainting via Latent h-Space Representation",

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