Image splicing localization using PCA-based noise level estimation

Abstract

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 kmeans 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.

Splicing Image and Its Localization: A Survey

“…However, those schemes fail to locate the spliced region when the noise level is quite small. Hence, a localization scheme based on PCA technology was proposed by analyzing the difference of image block noise levels in Zeng et al [Zeng, Zhan, Kang (2017)]. To preserve the structure information of image content, Chen used the location algorithm combining super-pixel segmentation and noise features in the literature in Chen et al [Chen, Zhao, Shi et al (2018)].…”

“…In general, there are two types of image tampering: image splicing [Ng and Chang (2004); Yuan and Ni (2017)] and copy-move [Arun (2015)]. However, some post-processing, including blurring, retouching, beautifying Zeng et al [Zeng, Zhan, Kang et al (2017)], rotating, etc., make the tampering traces in the tampered image to be eliminated. Considering the diversity of tampering operations [Asghar, Habib and Hussain (2016)], image forensics technology should adopt specific analysis solutions to improve the accuracy of forensics scheme.…”

Content-aware detection of JPEG grid inconsistencies for intuitive image forensics

“…Finally, notable approaches based on noise information include the method presented in [25] (NOI1), where the local image noise is isolated by wavelet filtering and local variance discrepancies are treated as indicative of tampering, [26] (NOI2) where the local image noise variance is modeled using the properties of the kurtosis of frequency sub-band coefficients in natural images, and [27] (NOI3), where, following extraction of the highfrequency residual using a high-pass filter, the information is modeled using a co-occurrence descriptor, and inconsistencies in the local statistical properties of the descriptor are used to detect spliced regions. A more recent approach [28] uses PCA-based noise level estimation, coupled with k-means clustering and and adaptive block segmentation to identify splices. Another relevant work is [29], where, besides analyzing the local noise variance, the local texture inhomogeneity is also estimated, since it tends to misguide the noise algorithm.…”

Hybrid Image-Retrieval Method for Image-Splicing Validation

“…These features were then utilized as the input for an iterative clustering algorithm to estimate the tampering mask. The difference between noise levels of tampered and original regions was employed to find the splicing traces [8,13]. The noise level was estimated using principal component analysis and then clustered by a k-means algorithm to localize the spliced regions [13].…”

“…The difference between noise levels of tampered and original regions was employed to find the splicing traces [8,13]. The noise level was estimated using principal component analysis and then clustered by a k-means algorithm to localize the spliced regions [13]. Nonlinear camera response function was individually used in Reference [14] or combined with noise-level function to exploit their strong relationship to localize the forged edges using a CNN [8].…”