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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| Published in: | Sensors (Basel, Switzerland) Vol. 20; no. 22; p. 6668 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Qiao, Tong Xu, Ming Yao, Hongwei Zheng, Ning Wu, Yiming |
| AuthorAffiliation | 2 Institute of Cyberspace Research, Zhejiang University, Hangzhou 310027, China 1 School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China; yaohomeway@gmail.com (H.Y.); mxu@hdu.edu.cn (M.X.); yimgwu@hotmail.com (Y.W.); nzheng@hdu.edu.cn (N.Z.) |
| AuthorAffiliation_xml | – name: 2 Institute of Cyberspace Research, Zhejiang University, Hangzhou 310027, China – name: 1 School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China; yaohomeway@gmail.com (H.Y.); mxu@hdu.edu.cn (M.X.); yimgwu@hotmail.com (Y.W.); nzheng@hdu.edu.cn (N.Z.) |
| Author_xml | – sequence: 1 givenname: Hongwei orcidid: 0000-0003-4680-5536 surname: Yao fullname: Yao, Hongwei – sequence: 2 givenname: Ming surname: Xu fullname: Xu, Ming – sequence: 3 givenname: Tong surname: Qiao fullname: Qiao, Tong – sequence: 4 givenname: Yiming surname: Wu fullname: Wu, Yiming – sequence: 5 givenname: Ning surname: Zheng fullname: Zheng, Ning |
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| SubjectTerms | 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 |
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| Title | Image Forgery Detection and Localization via a Reliability Fusion Map |
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