发表机构
Neural Defend Inc.（Neural Defend公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文跨基准家族评估Neural Defend ARCAS 1B深度伪造检测模型，保留原生聚合并补充记录级度量，以可追溯协议揭示测试群体差异，支持研究、平台安全与取证解读。
AI 中文摘要
AI生成图像的发展速度超过了针对特定基准的检测器评估，使得单一分数无法全面反映泛化能力。本文在不进行基准特定参数更新的情况下，跨基准家族评估了Neural Defend ARCAS 1B。我们保留了原生聚合方法，并辅以记录级度量、覆盖度核算和子组诊断。每个结果小节都明确了发布和评估群体，报告了官方指标，并描述了观察到的错误模式。综合分析综合了共同模式，同时保留了原生与汇总数量之间的区别。跨论文比较仅限于对齐的证据；发布、群体、预处理、训练或基准暴露的差异被视为背景而非排名依据。研究结果描述了在评估记录上的性能，而非普遍可靠性、校准、归因或未来的自适应攻击。通过将基准原生结果与汇总摘要区分开来，该研究使测试群体、类别平衡和缺失记录覆盖差异变得可见。它支持在研究、平台安全和取证审查环境中对检测器结果的解读，强调可追溯的协议条件而非声明或排行榜比较。
英文摘要
AI-generated imagery evolves faster than benchmark-specific detector evaluations, making a single score an incomplete account of generalization. This paper evaluates Neural Defend ARCAS 1B across benchmark families without benchmark-specific parameter updates. We retain native aggregation and supplement it with record-level measures, coverage accounting, and subgroup diagnostics. Each Results subsection identifies the release and evaluation population, reports the official metric, and describes observed error patterns. A combined analysis synthesizes shared patterns while preserving the distinction between native and pooled quantities. Cross-paper comparisons are restricted to aligned evidence; differences in release, population, preprocessing, training, or benchmark exposure are context rather than rank. The findings characterize performance on evaluated records, not universal reliability, calibration, attribution, or future adaptive attacks. By keeping benchmark-native outcomes distinct from pooled summaries, the study makes test-population, class-balance, and missing-record-coverage differences visible. It supports interpretation of detector results in research, platform-safety, and forensic-review settings, foregrounding traceable protocol conditions over claims or leaderboard comparisons.
