Image Forgery Detection and Localization via a Reliability Fusion Map

Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 22; p. 6668
Main Authors: Yao, Hongwei, Xu, Ming, Qiao, Tong, Wu, Yiming, Zheng, Ning
Format: Journal Article
Language:English
Published: Basel MDPI AG 21.11.2020
MDPI
Subjects:
Accuracy
Algorithms
convolution neural network (CNN)
Design
Digital cameras
digital image forensics
Forgery
Internet of Things
Multimedia
Neural networks
Noise
reliability fusion map (RFM)
Sensors
tampering detection and localization
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the goal of this paper is to design an effective detector capable of completing image forgery detection and localization. Specifically, relying on the designed constant high-pass filter, we first establish a well-performing CNN architecture to adaptively and automatically extract characteristics, and design a reliability fusion map (RFM) to improve localization resolution, and tamper detection accuracy. The extensive results from our empirical experiments demonstrate the effectiveness of our proposed RFM-based detector, and its better performance than other competing approaches.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20226668