Copy-Move Forgery Detection Based on Discrete and SURF Transforms

Al_azrak, Faten Maher; Elsharkawy, Zeinab F.; Elkorany, Ahmed S.; Banby, Ghada M. El; Dessowky, Moawad I.; El‐Samie, Fathi E. Abd

doi:10.1007/s11277-019-06739-7

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…𝐹 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 (𝑖) = {𝐹 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 (𝑖), 𝐹 𝑑𝑥 (𝑖), 𝐹 𝑑𝑦 (𝑖)}, (8) The gradient function 𝐹 𝑑𝑥 (𝑖) for grid unit, i in region x is represented as shown in Eq. ( 9)…”

Section: B) Feature Descriptormentioning

confidence: 99%

“…The matching process between the block of interest and its complementary images generates features in the form of a distance vector. For this vector, a correlation matrix is constructed, and singular value decomposition is utilized to extract the final set of features for classification [8]. Many researchers have focused on feature extraction tools combined with SURF.…”

Section: Introductionmentioning

confidence: 99%

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A Copy-Move Image Forgery Detection Using Modified SURF Features and A-KAZE Detector

2023

IJIES

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Copy-move forgery detection (CMFD) is a well-known digital image forgery technique that is used in detecting forgery regions of images. The copy-move forgery in images occurs when a specific section of an image is attached to a new component of the same image to replicate the forged image parts as an original. The forgery appears to be realistic because even after being forged in the objective region, the image acquires all the basic qualities of the original image. Some post-processing functions, such as scaling or rotation invariant, noise handling, hybrid image manipulation, etc., limit the capability of CMFD models. To overcome these limitations, the features are extracted from the Hybrid feature extraction using modified speeded up robust feature (M-SURF) with accelerated KAZE (A-KAZE) feature description and detection algorithm. The main advantages of the SURF descriptor are: the SURF features are resistant to several post-processing attacks and use Laplacian of gaussian to differentiate background and foreground features clearly. The A-KAZE descriptor is robust in detecting scale-invariant of forged regions and poorly localized key points of objects. A-KAZE constructs the invariant scaling levels using nonlinear diffusion filtering. It smooths the image while preserving the edges and decreasing speckle noise. As a result, it captures the precise location of features during the feature extraction process. Following the feature extraction, the matched features are compared, and removed false matches by applying random sample consensus (RANSAC) technique. The performance evaluation of the proposed Hybrid MSURF with A-KAZE CMFD method is validated on MICC-F220, MICC-F2000, CoMoFoD datasets and surpasses the existing CMFD methods. The proposed CMFD achieved better results compared to existing methods such as when CMFD based on DHE-SURF features along with mDBSCAN clustering and CMFD based on SURF, BRISK features along with DBSCAN clustering measured in terms of Precision of 99.93%, Accuracy of 99.46%, F1-score of 98.21%, true positive rate (TPR) of 99.93%, and false positive rate (FPR) of 4.67%.

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Section: B) Feature Descriptormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Copy-Move Image Forgery Detection Using Modified SURF Features and A-KAZE Detector

2023

IJIES

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“…These false matches can be eliminated by using morphological operators [21,22] , random sample consensus, or window method. In keypoint-based CMFD algorithms, keypoints are extracted from the suspected image by using scaleinvariant feature transform [23,24] , speeded-up robust feature (SURF) [25,26] , and binary robust invariant scalable keypoints [17] . Among these extracted keypoints, the similarity is verified, and false matches are eliminated using post-processing operators.…”

Section: Introductionmentioning

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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“…By the procedure of copy-move, an image may be counterfeited to hide or alter its sense [10,11]. It is complex to examine CMF with the naked eye if done carefully despite being one of the prevalent image modifications, along with it is intricate to prove the images' legitimacy for the forensic analyst [12,13]. So owing to these problems, the security and trustworthiness of media information are damaged [14].…”

Section: Introductionmentioning

confidence: 99%

Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

Dhanya¹,

Kalaiselvi²

2023

Computer Systems Science and Engineering

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A severe problem in modern information systems is Digital media tampering along with fake information. Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel. Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments. However, high complexity affects the developed methods. Presently, it is complicated to resolve the issue of the speed-accuracy trade-off. For tackling these challenges, this article put forward a quick and effective Copy-Move Forgery Detection (CMFD) system utilizing a novel Quad-sort Moth Flame (QMF) Light Gradient Boosting Machine (QMF-Light GBM). Utilizing Borel Transform (BT)-based Wiener Filter (BWF) and resizing, the input images are initially pre-processed by eliminating the noise in the proposed system. After that, by utilizing the Orientation Preserving Simple Linear Iterative Clustering (OPSLIC), the pre-processed images, partitioned into a number of grids, are segmented. Next, as of the segmented images, the significant features are extracted along with the feature's distance is calculated and matched with the input images. Next, utilizing the Union Topological Measure of Pattern Diversity (UTMOPD) method, the false positive matches that took place throughout the matching process are eliminated. After that, utilizing the QMF-Light GBM visualization, the visualization of forged in conjunction with non-forged images is performed. The extensive experiments revealed that concerning detection accuracy, the proposed system could be extremely precise when contrasted to some top-notch approaches.

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Copy-Move Forgery Detection Based on Discrete and SURF Transforms

Cited by 11 publications

References 27 publications

A Copy-Move Image Forgery Detection Using Modified SURF Features and A-KAZE Detector

A Copy-Move Image Forgery Detection Using Modified SURF Features and A-KAZE Detector

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

Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

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