Abstract
With the development of a variety of image editing tools, performing digital image forgery and concealing the forgery edge is easier. On the other hand, these visually convincing tampering operations also make authentication of digital images difficult. Therefore, developing a precise and robust method to detect these splicing images is urgently required. In the past, some researchers proposed some methods, which achieves an accuracy of over 97%, but robustness of these methods remains unknown. In this paper, a novel image forgery detection method based on a special blocking strategy is proposed, in which the processing unit for each block is a rich model convolutional neural network (rCNN). The proposed method is not only able to detect the splicing image but also reserves its effectiveness under circumstances of JPG compression. Extensive experiments with CASIA v1.0, CASIO v2.0 and Columbia image forgery evaluation databases were carried out, which demonstrates the effectiveness and strong robustness of the proposed method.
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Zhou, J., Ni, J., Rao, Y. (2017). Block-Based Convolutional Neural Network for Image Forgery Detection. In: Kraetzer, C., Shi, YQ., Dittmann, J., Kim, H. (eds) Digital Forensics and Watermarking. IWDW 2017. Lecture Notes in Computer Science(), vol 10431. Springer, Cham. https://doi.org/10.1007/978-3-319-64185-0_6
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DOI: https://doi.org/10.1007/978-3-319-64185-0_6
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