Detecting Image Manipulation
9 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Detecting Image Manipulation
Columbia (OSN-transmitted - Facebook)
Most implemented papers
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.
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations
Face manipulation methods can be misused to affect an individual's privacy or to spread disinformation.
Deep PCB To COCO Convertor
It has 1500 image pairs.
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.
FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects
The internet is filled with fake face images and videos synthesized by deep generative models.
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.
MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization
Recent image manipulation localization and detection techniques typically leverage forensic artifacts and traces that are produced by a noise-sensitive filter, such as SRM or Bayar convolution.
Digital Image Forensics: A quantitative & qualitative comparison between State-of-the-art-AI and Traditional Techniques for detection and localization of image manipulations
Using Digital Image Forensics, the primary objective of this work is to conduct a comprehensive quantitative and qualitative study that compares traditional forensic techniques to a state-of-the-art AI based approach.
