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+
Latest papers
Augmented Balanced Image Dataset Generator Using AugStatic Library
avs-abhishek123/Balanced_Augmented_ImageDatasetGenerator
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International Journal Of Research And Analytical Reviews (IJRAR) 2022
This paper focuses on the image dataset generator that balances an imbalanced dataset using the AugStatic augmentation library.
Proactive Image Manipulation Detection
vishal3477/proactive_imd
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CVPR 2022
That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one.
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.
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
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.
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization
To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations.
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.
Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples
We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features.
Localization of Deep Inpainting Using High-Pass Fully Convolutional Network
lihaod/Deep_inpainting_localization
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ICCV 2019
The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals.
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.
