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Evidence record 14583 · automatically gathered

FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking

Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI models can now inject localized, high-fidelity manipulations, creating deceptive attacks that bypass standard verification. Training robust image forensic models to detect these anomalies is hindered by privacy regulations, forcing reliance on synthetic templates lacking the intricate visual patterns of real IDs. To bridge this domain gap, we intr

Record details

Published: 29 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy
Retrieved: 30 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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Shared topic: Regulation · Shared topic: Privacy

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Shared topic: Regulation · Shared topic: Privacy

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Shared topic: Regulation · Shared topic: Privacy

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arXiv cs.CY · 28 July 2026

Shared topic: Regulation · Shared topic: Privacy

Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents

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Shared topic: Regulation · Shared topic: Privacy

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How to cite this record

ethics.ai (29 July 2026), “FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking,” evidence record 14583, https://ethics.ai/record/14583 (originally published by arXiv).

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