Abstract
Fully convolutional networks (FCNs) have been efficiently applied in splicing localization. However, the existing FCN-based methods still have three drawbacks: (a) their performance in detecting image details is unsatisfactory; (b) deep FCNs are difficult to train; (c) results of multiple FCNs are merged using fixed parameters to weigh their contributions. So, an improved method is proposed. Firstly, both the original spliced image and its corresponding residual image are regarded as the inputs of the network. Secondly, the residual block is introduced into FCN as residual-based FCN (RFCN) to make the network easier to optimize. Thirdly, three different RFCNs are merged to enhance locating maps with two learnable weight parameters. Besides, condition random field is introduced into the whole network to improve the results further. Experimental results on five datasets show that the proposed method performs better than some existing methods in localization ability, generalization ability, and robustness against additional operations.
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