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
Neural Imaging Pipelines - the Scourge or Hope of Forensics?
This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel, where state-of-the-art forensic techniques fail.
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.
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.
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.
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.
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.
AugStatic - A Light-Weight Image Augmentation Library
avs-abhishek123/AugStatic
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Journal of Emerging Technologies and Innovative Research (JETIR) 2022
AugStatic is a custom-built image augmentation library with lower computation costs and more extraordinary salient features compared to other image augmentation libraries.
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.
Noise and Edge Based Dual Branch Image Manipulation Detection
In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subtle traces of manipulation.
Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
sandy-zeng/pcl
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Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image.
