arXiv Computer Vision
Oct 9

WorldFact-Bench: Beyond Image-Internal Plausibility to Image-World Consistency

WorldFact-Bench is a new benchmark that tests image‑world consistency by presenting 1,274 real‑fake image pairs across four verification regimes and ten semantic domains, each pair containing a factual conflict while keeping the image visually plausible. The evaluation requires both images in a pair to be classified correctly, and the benchmark focuses on single‑image assessment without predefined claims. The authors also introduce PERSIST‑Agent, an iterative verification system that maintains a persistent state linking candidate facts, visual observations, evidence, and verification statuses, and shows improved pair accuracy over baseline methods on 8B backbones.

By Zhuohong Chen, Zhengxian Wu, Yunyao Yu, Hangrui Xu, Zijian Yu, Hao Tan, Zhifang Liu, Peng Jiao, Jun Lan, Haoqian Wang
arXiv AI
Sep 18

FORGE: Forensic Reasoning with Grounded Evidence

FORGE is a forensic deepfake analysis system that provides region‑grounded natural language explanations for image manipulations. It addresses the inductive bias mismatch of multimodal large language models by adding a Vision‑Only Model trained on dense patch prediction, allowing the language model to interleave tokens with preserved spatial correspondence. Across face‑manipulated and fully synthetic content, FORGE delivers fine‑grained attribute queries and outperforms in‑domain baselines, with region‑specific evaluation and human studies confirming explanation faithfulness.

By Rohit Kundu, Shan Jia, Vishal Mohanty, Athula Balachandran, Amit K. Roy-Chowdhury
arXiv Computer Vision
Oct 8

MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection

The MSU team presents a modular approach to the Explainable Deepfake Detection Challenge, combining multiple DINOv3 backbones with Mesorch manipulation-localization features for real/fake classification. They incorporate a Grounding-DINO-based pseudo-mask pipeline to generate artifact evidence maps and a local contrastive objective to separate artifact from authenticity cues. For explanations, class-conditional Qwen3-VL models produce complex descriptions, which are then simplified by a GRPO-optimized text model, achieving high detection and explanation scores on the XPlainVerse dataset.

By Artem Filippov, Aleksandr Gushchin, Kirill Koltsov, Dmitriy Vatolin, Anastasia Antsiferova