Image Manipulation Detection
26 papers with code • 5 benchmarks • 2 datasets
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.
Benchmarks
These leaderboards are used to track progress in Image Manipulation Detection
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Libraries
Use these libraries to find Image Manipulation Detection models and implementations
Casia V1+
Most implemented papers
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features
To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net.
Learning Rich Features for Image Manipulation Detection
LarryJiang134/Image_manipulation_detection
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CVPR 2018
Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.
Detecting Photoshopped Faces by Scripting Photoshop
PeterWang512/FALdetector
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ICCV 2019
Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop.
Video Face Manipulation Detection Through Ensemble of CNNs
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.
Image Manipulation Detection by Multi-View Multi-Scale Supervision
The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images.
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
dong03/MVSS-Net
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As both clues are meant to be semantic-agnostic, the learned features are thus generalizable.
Deep PCB To COCO Convertor
It has 1500 image pairs.
Explicit Visual Prompting for Universal Foreground Segmentations
nifangbaage/explicit-visual-prompt
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We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP).
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels
pkorus/neural-imaging
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CVPR 2019
Forensic analysis of digital photo provenance relies on intrinsic traces left in the photograph at the time of its acquisition.
