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 with no code
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset
This paper highlights the addition of a sequential layer to the traditional RESNET 18 model for computing the accuracy of an Image classification dataset.
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
The accuracies are acquired for each augmentation technique using a RESNET18 model.
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
A painful element of real data is that it tends to be imbalanced.
ObjectFormer for Image Manipulation Detection and Localization
Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection.
SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization
Recent algorithms for image manipulation detection almost exclusively use deep network models.
Crowd-powered Face Manipulation Detection: Fusing Human Examiner Decisions
To this end, various decision fusion methods are proposed incorporating the examiners' decision confidence, experience level, and their time to take a decision.
Learning Hierarchical Graph Representation for Image Manipulation Detection
The objective of image manipulation detection is to identify and locate the manipulated regions in the images.
Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices
While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations.
Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching
Results obtained from challenging cross-database experiments in which the analyzed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilizing deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance.
L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection
Furthermore, we attain an overall accuracy of 99. 68% in image manipulation detection, which implies that it can be used as a general-purpose network for image forensic tasks.
