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Multi-scale noise estimation for image splicing forgery detection

机译：用于图像拼接伪造检测的多尺度噪声估计

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摘要

Noise discrepancies in multiple scales are utilized as indicators for image splicing forgery detection in this paper. Specifically, the test image is initially segmented into superpixels of multiple scales. In each individual scale, noise level function, which reflects the relation between noise level and brightness of each segment, is computed. Those segments not constrained by the noise level function are regarded as suspicious regions. In the final step, pixels appears in suspicious regions of each scale, after necessary morphological processing, are marked as spliced region(s). The Optimal Parameter Combination Searching (OPCS) Algorithm is proposed to determine the optimal parameters during the process. Two datasets are created for training the optimal parameters and to evaluate the proposed scheme, respectively. The experimental results show that the proposed scheme is effective, especially for the multi-objects splicing. In addition, the proposed scheme is proven to be superior to the existing state-of-the-art method. (C) 2016 Elsevier Inc. All rights reserved.
机译：本文利用多尺度的噪声差异作为图像拼接伪造检测的指标。具体来说，首先将测试图像分割成多个比例的超像素。在每个单独的标度中，计算反映噪声水平和每个片段的亮度之间关系的噪声水平函数。那些不受噪声水平函数约束的部分被视为可疑区域。在最后一步中，经过必要的形态学处理后，像素出现在每个刻度的可疑区域，被标记为拼接区域。提出了最优参数组合搜索（OPCS）算法来确定过程中的最优参数。创建了两个数据集，分别用于训练最佳参数和评估所提出的方案。实验结果表明，该方案是有效的，特别是对于多对象拼接。此外，该方案被证明优于现有的最新技术。 （C）2016 Elsevier Inc.保留所有权利。

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