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

arXiv: https://arxiv.org/abs/2609.25154 · HTML: https://mlsanity.com/paper/2609.25154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [A Sanity Check for AI-generated Image Detection](https://mlsanity.com/paper/2406.19435/) (2024)
- [Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection](https://mlsanity.com/paper/2312.16649/) (2023)
- [PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection](https://mlsanity.com/paper/2311.12397/) (2023)
- [Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective](https://mlsanity.com/paper/2408.06741/) (2024)
- [Is Artificial Intelligence Generated Image Detection a Solved Problem?](https://mlsanity.com/paper/2505.12335/) (2025)
- [Community Forensics: Using Thousands of Generators to Train Fake Image Detectors](https://mlsanity.com/paper/2411.04125/) (2024)
- [How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study](https://mlsanity.com/paper/2602.07814/) (2026)
- [MIRROR: Manifold Ideal Reference ReconstructOR for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2602.02222/) (2026)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective](https://mlsanity.com/paper/2512.05651/) (2025)
- [SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images](https://mlsanity.com/paper/2602.20412/) (2026)
- [HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild](https://mlsanity.com/paper/2604.03555/) (2026)
- [TAP into the Patch Tokens: Leveraging Vision Foundation Model Features for AI-Generated Image Detection](https://mlsanity.com/paper/2604.26772/) (2026)
- [Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection](https://mlsanity.com/paper/2604.27875/) (2026)

## Abstract (arXiv)

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.