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DEFACTO: Image and Face Manipulation Dataset

Abstract : This paper presents a novel dataset for image and face manipulation detection and localization called DEFACTO. The dataset was automatically generated using Microsoft common object in context database (MSCOCO) to produce semantically meaningful forgeries. Four categories of forgeries have been generated. Splicing forgeries which consist of inserting an external element into an image, copy-move forgeries where an element within an image is duplicated, object removal forgeries where objects are removed from images and lastly morphing where two images are warped and blended together. Over 200000 images have been generated and each image is accompanied by several annotations allowing precise localization of the forgery and information about the tampering process.
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Contributor : Jean-Baptiste VU VAN Connect in order to contact the contributor
Submitted on : Thursday, November 21, 2019 - 9:22:46 AM
Last modification on : Wednesday, August 31, 2022 - 6:56:29 PM




Gael Mahfoudi, Badr Tajini, Florent Retraint, Frederic Morain-Nicolier, Jean-Luc Dugelay, et al.. DEFACTO: Image and Face Manipulation Dataset. European Signal Processing Conference (EUSIPCO), 2019, A Coruna, Spain. ⟨10.23919/EUSIPCO.2019.8903181⟩. ⟨hal-02373490⟩



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