Copy-move forgery detection using local tetra pattern based texture descriptor

Ganguly, Sagnik; Mandal, Sanmit; Malakar, Samir; Sarkar, Ram

doi:10.1007/s11042-022-14287-9

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Normalization ensures that GLCM values lie in the range [0, 1] and makes the features invariant to changes in image contrast and brightness. Normalization is typically done by dividing the GLCM by the sum of all its elements: 𝑃 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 (𝑖, 𝑗|𝑑, 𝜃) = 𝑃(𝑖, 𝑗|𝑑, 𝜃) ∑ ∑ 𝑃(𝑖, 𝑗|𝑑, 𝜃) 𝑗 𝑖 (13)…”

Section: Normalize the Glcmmentioning

confidence: 99%

Hybrid Feature Selection for Effective Copy-Move Forgery Detection

2024

IJIES

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In today's digital landscape, the threat of digital image forgery is on the rise, necessitating the development of advanced detection and authentication techniques. Copy-move image forgery, a prevalent form of manipulation, involves duplicating a portion of an image and inserting it elsewhere with malicious intent. Detecting such forgeries is of utmost importance. This research introduces an innovative approach that combines convolutional neural network (CNN)-based deep features and gray-level co-occurrence matrix (GLCM) features. These hybrid features are carefully selected using neighborhood component analysis (NCA) to optimize their discriminative power. Subsequently, a support vector machine (SVM) classifier is employed to classify these refined hybrid features, resulting in exceptional detection performance. The proposed method achieves remarkable accuracy in forgery detection. In the CASIA 1 dataset, the hybrid features-NCA-SVM method outperforms other techniques with an accuracy of 97.62%. Similarly, on the MICC-F220 dataset, the hybrid features-NCA-SVM approach attains the highest accuracy of 98.00%. These results underscore the robustness and versatility of the proposed method in detecting copy-move forgeries across different datasets.

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Section: Normalize the Glcmmentioning

confidence: 99%

Hybrid Feature Selection for Effective Copy-Move Forgery Detection

2024

IJIES

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“…The localization of forged pixels is realized via a Ciratefi based approach. Local tetra pattern (LTrP) based feature extraction is developed in [11] for forgery detection and localization. Firstly, the input image is divided into non-overlapping blocks and then from each individual block LTrP descriptors are extracted.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Image Authentication Algorithm using Redundant Wavelet Transform Based Sift Descriptors and Complex Zernike Moments

Kallath¹,

Lakshmisudha²

2023

IJACSA

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Due to the advanced multimedia editing tools and supported by sophisticated hardware, creating image/video manipulations for malicious purposes is increasing which is almost impossible to detect manually. Moreover, to conceal the traces, different post-processing operations are performed. Therefore, authenticity is a growing concern and important for identifying original and forged images. One of the popular image manipulations is copy-move forgery in which one or more regions in the image are duplicated to create a malicious effect within an image. The work in this article presents redundant wavelet transform based complex Zernike moment and Scale Invariant Feature Transform (SIFT) keypoint matching technique for copy-move image forgery operation detection. SIFT is robust against scale and rotation that works on identifying similarity using exhaustive search of SIFT features. After extracting SIFT keypoint features agglomerative hierarchical clustering is employed for grouping and key point matching operation is performed. Finally, block matching operation is evaluated and forged regions in the manipulated images are marked and displayed. This work also presents optimized SIFT key-point feature computations resulting in lower computation time, often one of the requirements in real time deployment. The proposed algorithm performance is evaluated using Precision, Recall, F-Measure and average detection accuracy on popular and publicly available MICC-220 database. The proposed technique demonstrates improved speedup and detection rate compared to existing approaches.

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“…Advances in computerized methods have been made possible by developments in Computer Vision (CV), Machine Learning (ML), and Artificial Intelligence (AI), which have made it possible to precisely and precisely used in agriculture [7,8], smart cities [9], skin cancer [10,11], facial identification [12,13], image splicing [14] and forgery detection [15] in images. In 10 years, ML and artificial intelligence developments have accomplished an enormous interest in the openness of a few highvelocity figuring gadgets and processors that have further developed the handling time, unwavering quality, and precision of the outcomes/yield got.…”

Section: Introductionmentioning

confidence: 99%

Weber Law Based Approach for Multi-Class Image Forgery Detection

Akram,

Rashid,

Jaffar

et al. 2024

CMC

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Today's forensic science introduces a new research area for digital image analysis for multimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or create misleading publicity by using tempered images. Exiting forgery detection methods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybrid method for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal blocks to get scale-invariant features. Weber law has been used for getting texture features, and finally, XGBOOST is used to classify both Copy-Move and splicing forgery. The proposed method classified three types of forgeries, i.e., splicing, Copy-Move, and healthy. Benchmarked (CASIA 2.0, MICCF200) and RCMFD datasets are used for training and testing. On average, the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation, which is far better than existing methods.

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Copy-move forgery detection using local tetra pattern based texture descriptor

Cited by 7 publications

References 43 publications

Hybrid Feature Selection for Effective Copy-Move Forgery Detection

Hybrid Feature Selection for Effective Copy-Move Forgery Detection

Optimized Image Authentication Algorithm using Redundant Wavelet Transform Based Sift Descriptors and Complex Zernike Moments

Weber Law Based Approach for Multi-Class Image Forgery Detection

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