Abstract
Aiming at the problem of low accuracy, high computational complexity and incomplete edge information of most image splicing localization algorithm, this paper proposes a new image splicing localization algorithm. First, the SLIC image segmentation algorithm is used to segment the image. Secondly, the noise estimation value of each super-pixel block is calculated by the FAST noise estimation algorithm. Then, weight of each image block is calculated through noise and image features. Finally, the noise value sequence is processed by clustering and statistical processing to determine the pixels of the background area and the splicing area, thus the splicing area is located. In this paper, the algorithm is tested on the color image database of Columbia, and compared with the existing image splicing localization algorithms based on block-segmentation and based on pixel. The experiment shows that the proposed algorithm can preserve the connection between image features, hold the edge of the splicing area, and effectively improve the efficiency of localization detection under the premise of ensuring the accuracy of image splicing localization.
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Chen, H., Zhao, C., Shi, Z., Zhu, F. (2018). An Image Splicing Localization Algorithm Based on SLIC and Image Features. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_56
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