{
  "id": 19181,
  "url": "https://arxiv.org/abs/2608.13100v1",
  "title": "Multi-Layer Context Camouflaging: A Semantic Superposition and Contextual Lamination Framework for Malpractice-Resilient Online Assessment",
  "summary": "Contemporary online assessment systems rely primarily on browser lockdown, webcam monitoring, and behavioural analytics, yet remain vulnerable to attacks that extract the assessment content itself through screenshots, screen sharing, optical character recognition, and automated scraping. This paper extends the Multi-dimensional Spatio-Temporal Context Camouflaging Model (MSCCM) within the MARS (Multi-modal Assessment Resilience Suite) by introducing the Multi-Layer Context Camouflaging Theory (M",
  "authors": "Gupta Lovi Raj, Kaur Kamalpreet, Dama Sri Ram, Parani Prajithaa",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T11:25:12.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19181",
  "original_url": "https://arxiv.org/abs/2608.13100v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}