Image Forgery Detection Algorithm Based on Cascaded Convolutional Neural Network

Xiuli BI; Yang WEI; Bin XIAO; Weisheng LI; Jianfeng MA

doi:10.11999/JEIT190043

Volume 41 Issue 12

Dec. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(12): 2987-2994

Xiuli BI, Yang WEI, Bin XIAO, Weisheng LI, Jianfeng MA. Image Forgery Detection Algorithm Based on Cascaded Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2987-2994. doi: 10.11999/JEIT190043

Citation:

Xiuli BI, Yang WEI, Bin XIAO, Weisheng LI, Jianfeng MA. Image Forgery Detection Algorithm Based on Cascaded Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2987-2994. doi: 10.11999/JEIT190043

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Image Forgery Detection Algorithm Based on Cascaded Convolutional Neural Network

doi: 10.11999/JEIT190043

1.
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Computer Science and Technology, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61572092, U1401252), The National Science & Technology Major Project (2016YFC1000307-3)

Received Date: 2019-01-15
Rev Recd Date: 2019-04-19

Available Online: 2019-05-21

Publish Date: 2019-12-01

Abstract

Abstract

The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.

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References(17)

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