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
Deep Image Composition Meets Image Forgery
Unlike other automated data generation frameworks, we use state of the art image composition deep learning models to generate spliced images close to the quality of real-life manipulations.
Exploring Multi-Modal Fusion for Image Manipulation Detection and Localization
Recent image manipulation localization and detection techniques usually leverage forensic artifacts and traces that are produced by a noise-sensitive filter, such as SRM and Bayar convolution.
A New Benchmark and Model for Challenging Image Manipulation Detection
Existing Image Manipulation Detection (IMD) methods are mainly based on detecting anomalous features arisen from image editing or double compression artifacts.
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
shngjz/wildcamera
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NeurIPS 2023
3D sensing for monocular in-the-wild images, e. g., depth estimation and 3D object detection, has become increasingly important.
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).
Explicit Visual Prompting for Low-Level Structure Segmentations
nifangbaage/explicit-visual-prompt
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CVPR 2023
Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i. e., the features from frozen patch embeddings and the input's high-frequency components.
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.
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.
Deep PCB To COCO Convertor
It has 1500 image pairs.
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.
