Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization

Srivastava, Rajeev; Khare, Ashish

doi:10.5815/ijigsp.2015.10.08

Cited by 29 publications

(3 citation statements)

References 31 publications

(95 reference statements)

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“…The method achieved 98.54% and 97.73 accuracy rates on the CASIA v1.0 and CASIA v2.0 datasets. LBP, Local Phase Quantization (LPQ), and Weber Local Descriptor (WLD) are used as texture descriptors for feature extracting from the images (Shen et al 2017, Agarwal and Chand 2015, Hussain et al 2013, Alahmadi et al 2017. Agarwal et al (2015) proposed a method based on Entropy Filter and LPQ.…”

Section: Introductionmentioning

confidence: 99%

“…LBP, Local Phase Quantization (LPQ), and Weber Local Descriptor (WLD) are used as texture descriptors for feature extracting from the images (Shen et al 2017, Agarwal and Chand 2015, Hussain et al 2013, Alahmadi et al 2017. Agarwal et al (2015) proposed a method based on Entropy Filter and LPQ. An entropy filter is applied to the image at various scales, and LPQ is applied to extract texture features.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, linear and logistic regression support only linear solutions, and outliers are better-taken care of by SVM than KNN (k-nearest neighbor). Studies on the splicing forgery in the literature such as Muhammed et al 2014, Shen et al 2017, Agarwal and Chand 2015, Hussain et al 2013, Alahmadi et al 2017, He et al 2012, Li et al 2017, Odabas Yildirim and Ulutas 2019, are also used SVM to authenticate the images as can be seen in Table 1. So, in this study, the performance of the SVM in classifying the blocks is researched.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

Yildirim

Tahaoglu

Ulutaş

et al. 2023

Wireless Pers Commun

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With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the stateof-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.

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