LATIDIA · Ciberseguridad

Benchmarking Neural Defend ARCAS 1B: A Foundational Multimodal Deepfake Detection Model

arXiv: 2609.25154v1Tipo de anuncio: nuevo Resumen: las imágenes generadas por IA evolucionan más rápido que las evaluaciones de detectores específicos de referencia, lo que hace que una sola puntuación sea una descripción incompleta de la generalización. En este documento se evalúa la función

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arXiv:2609.25154v1 Announce Type: new Abstract: 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