SOTAVerified

Image Manipulation Detection

The task of detecting images or image parts that have been tampered or manipulated (sometimes also referred to as doctored). This typically encompasses image splicing, copy-move, or image inpainting.

Papers

Showing 1–10 of 73 papers

TitleStatusHype
Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Features—0
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and LocalizationCode2
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era—0
Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement—0
LEGION: Learning to Ground and Explain for Synthetic Image Detection—0
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning—0
Data-Driven Fairness Generalization for Deepfake Detection—0
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation DetectionCode1
HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning—0
Perturb, Attend, Detect and Localize (PADL): Robust Proactive Image Defense—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Early FusionBalanced Accuracy0.77—Unverified
2Late FusionBalanced Accuracy0.72—Unverified
3TruForBalanced Accuracy0.68—Unverified
4CAT-Net v2Balanced Accuracy0.64—Unverified
5MVSS-NetBalanced Accuracy0.51—Unverified
6ManTraNetBalanced Accuracy0.5—Unverified
7CR-CNNBalanced Accuracy0.39—Unverified
8SPANBalanced Accuracy0.24—Unverified