Image Forgery Detection
12 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Image Forgery Detection
Most implemented papers
Boundary-based Image Forgery Detection by Fast Shallow CNN
In this paper, we substantiate that Fast SCNN can detect drastic change of chroma and saturation.
Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier
Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene.
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
FrancescoMarra/E2E-ForgeryDetection
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Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.
Fake face detection via adaptive manipulation traces extraction network
EricGzq/AMTENnet
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Thus, we propose an adaptive manipulation traces extraction network (AMTEN), which serves as pre-processing to suppress image content and highlight manipulation traces.
Forgery Blind Inspection for Detecting Manipulations of Gel Electrophoresis Images
Recently, falsified images have been found in papers involved in research misconducts.
Analysing Statistical methods for Automatic Detection of Image Forgery
Image manipulation and forgery detection have been a topic of research for more than a decade now.
Robust Image Forgery Detection Over Online Social Network Shared Images
highwaywu/imageforensicsosn
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CVPR 2022
To fight against the OSN-shared forgeries, in this work, a novel robust training scheme is proposed.
Comprint: Image Forgery Detection and Localization using Compression Fingerprints
In an attempt to fight fake news, forgery detection and localization methods were designed.
Hierarchical Fine-Grained Image Forgery Detection and Localization
chelsea234/hifi_ifdl
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CVPR 2023
As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation.
Rethinking Image Forgery Detection via Contrastive Learning and Unsupervised Clustering
highwaywu/focal
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To resolve this dilemma, we propose the FOrensic ContrAstive cLustering (FOCAL) method, a novel, simple yet very effective paradigm based on contrastive learning and unsupervised clustering for the image forgery detection.
