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Image splicing localization using PCA-based noise level estimation
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PRE
AI
DOI：10.1007/s11042-016-3712-8.png)
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Abstract
En 中文
Image splicing is one of the most common image tampering operations, where the content of the tampered image usually significantly differs from that of the original one. As a consequence, forensic methods aiming to locate the spliced areas are of great realistic significance. Among these methods, the noise based ones, which utilize the fact that images from different sources tend to have various noise levels, have drawn much attention due to their convenience to implement and the relaxation of some operation specific assumptions. However, the performances of the existing noise based image splicing localization methods are unsatisfactory when the noise difference between the original and spliced regions is relatively small. In this paper, through incorporation of a recent developed noise level estimation algorithm, we propose an effective image splicing localization method. The proposed method performs blockwise noise level estimation of a test image with principal component analysis (PCA)-based algorithm, and segments the tampered region from the original region by k-means clustering. The experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods, especially for practical image splicing, where the noise difference between the original and spliced regions is typically small.
Keywords:
Image splicing
Image splicing localization
Noise level
Principal component analysis (PCA)
K-means clustering
Journal
IF:
3
Papers:
2.0W
Citations:
3.2W
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