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Image Manipulation Detection
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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.
논문
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ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
DF-Net: The Digital Forensics Network for Image Forgery Detection
Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement
LEGION: Learning to Ground and Explain for Synthetic Image Detection
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Data-Driven Fairness Generalization for Deepfake Detection
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning
Perturb, Attend, Detect and Localize (PADL): Robust Proactive Image Defense
FastForensics: Efficient Two-Stream Design for Real-Time Image Manipulation Detection
Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
Deep Image Restoration For Image Anti-Forensics
Deep Image Composition Meets Image Forgery
Exploring Saliency Bias in Manipulation Detection
MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization
A New Benchmark and Model for Challenging Image Manipulation Detection
Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Explicit Visual Prompting for Universal Foreground Segmentations
On the Effectiveness of Image Manipulation Detection in the Age of Social Media
Explicit Visual Prompting for Low-Level Structure Segmentations
TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization
Auto-Focus Contrastive Learning for Image Manipulation Detection
MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization
Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
Noise and Edge Based Dual Branch Image Manipulation Detection
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
Proactive Image Manipulation Detection
ObjectFormer for Image Manipulation Detection and Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.