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Open AccessArticle

Image Forgery Detection and Localization via a Reliability Fusion Map

1
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
2
Institute of Cyberspace Research, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6668; https://doi.org/10.3390/s20226668
Received: 22 October 2020 / Revised: 18 November 2020 / Accepted: 19 November 2020 / Published: 21 November 2020
(This article belongs to the Section Sensing and Imaging)
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. View Full-Text
Keywords: digital image forensics; tampering detection and localization; convolution neural network (CNN); reliability fusion map (RFM) digital image forensics; tampering detection and localization; convolution neural network (CNN); reliability fusion map (RFM)
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Yao, H.; Xu, M.; Qiao, T.; Wu, Y.; Zheng, N. Image Forgery Detection and Localization via a Reliability Fusion Map. Sensors 2020, 20, 6668.

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