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arXiv AI

Geometric Inconsistency Localization in Multi-View Image Sets

The paper introduces DeformView, a wide‑baseline multi‑view dataset with pixel‑level annotations of geometric inconsistencies, and evaluates existing multi‑view consistency‑scoring methods, finding they transfer poorly to forensic localization tasks. To address this, the authors propose DEFECt3R, a lightweight learning‑based classifier that leverages cross‑view feature relationships and hard negative supervision to localize inconsistencies at the pixel level, achieving better performance and fewer false positives. Ablation studies confirm the importance of feature representations and correspondence quality for localization.

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arXiv Computer Vision
Aug 28

GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

GeoMAD is a multi‑view anomaly detection framework that fuses multiple camera viewpoints while maintaining geometric awareness and scalability to multi‑class industrial settings. It introduces a Cross‑view Deformable Fusion Module (CDFM) that learns view‑pair‑specific sampling offsets on 2D feature maps, enabling hierarchical cross‑view correspondence without camera calibration or voxel construction. Additionally, Distributional View Alignment (DVA) provides a self‑supervised loss that aligns bottleneck distributions across views, ensuring global consistency without pixel‑level correspondence. Together, CDFM and DVA achieve geometry‑aware, distribution‑consistent fusion and demonstrate strong detection and localization performance on Real‑IAD and MANTA‑Tiny datasets.

By Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng, Kai-Lung Hua
safety
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arXiv Computer Vision
Sep 3

From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images

The paper proposes a unified forensics framework that extends traditional binary image manipulation detection to a multiclass setting—distinguishing real, fully synthetic, and tampered images. It adds a segmentation branch for pixel‑level localization of tampered regions, achieving higher classification accuracy and IoU scores compared to recent benchmarks. The authors provide the implementation on GitHub for reproducibility.

By Annalisa Gallina, Marco Fiorucci, Marco Brigo, Federica Battisti, Lamberto Ballan
computer-visionroboticsbenchmarks
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arXiv Computer Vision
4d ago

ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos

ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.

By Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma, Conghui He, Weijia Li
llmscomputer-visionroboticsmultimodalbenchmarks
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arXiv Computer Vision
Sep 2

Mind the Rift: Cross-Scale Coupling Mismatch for AI-Generated Video Detection

The paper introduces RIFT, a forensic framework for detecting AI-generated videos by exploiting a cross‑scale coupling mismatch between macro‑level temporal dynamics and micro‑level pixel residuals. RIFT comprises a macro stream that models expected temporal evolution, a micro stream that probes residual patterns, and a coupling divergence module that quantifies their conditional dependency. Experiments on VidProM and GenVidBench show near‑perfect F1‑scores and robust performance across different encoders.

By Siyu Li, Jin Yang, Weiheng Liang
benchmarks
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arXiv Computer Vision
Sep 1

Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction

arXiv:2608.29211v1 Announce Type: new Abstract: Ground image localization with respect to satellite imagery is a key enabler for metrically-accurate, geo-localized 3D scene reconstruction from uncons...

By Angel Daruna, Ben Southall, Niluthpol Chowdhury Mithun, Kshitij Minhas, Nicholas Meegan, Qiao Wang, Bogdan Matei, Supun Samarasekera, Rakesh Kumar
safety
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arXiv Computer Vision
Aug 31

FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization

FUSED is a new framework that jointly detects and localizes AI-generated inpainting by combining low-level forensic cues with high-level semantic features through a sparsely-gated Mixture-of-Experts architecture. It predicts both an image-level manipulation score and a pixel-level mask of the inpainted region. On the OpenSDID cross-generator benchmark, FUSED outperforms existing methods, especially on unseen generators, and transfers effectively to the AutoSplice and CocoGlide benchmarks, doubling localization performance.

By Anton Nuzhdin, Marcel Worring, Ivona Najdenkoska
diffusionroboticsbenchmarks
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arXiv AI
Jun 2

CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection

arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.

By Huidong Feng, Wentao Chen, Jie Chen, Xinqi Cai, Ruolong Ma, Yinglin Zheng, Yuxin Lin, Ming Zeng
llmsdiffusionmultimodalbenchmarks
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