Copy Move Forgery Detection Using GLCM Based Statistical Features

Suresh, G.; Rao, Chanamallu Srinivasa

doi:10.5121/ijci.2016.5419

Cited by 12 publications

(2 citation statements)

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“…GLCM is also used to calculate the remaining features, but the above mentioned are commonly used powerful features [38] . Fourier phase spectrum (FPS) has the great advantage of blur invariance, which is effectively used in LPQ.…”

Section: Feature Extractionmentioning

confidence: 99%

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Babu

Rao

2023

Big Data Min. Anal.

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Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

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Section: Feature Extractionmentioning

confidence: 99%

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Babu

Rao

2023

Big Data Min. Anal.

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“…For each the gray image blocks, LBP of magnitude of 2D-DCT coefficient are extracted. Suresh and Rao [5], suggested CMFD based on gray-level co-occurrence matrix (GLCM) texture features. In one direction, 22 statistical features are calculated.…”

Section: Introductionmentioning

confidence: 99%

Forgery detection algorithm based on texture features

Ahmed

Hammad

Jamil

2021

IJEECS

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Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques.

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