An integrated method of copy-move and splicing for image forgery detection

Prakash, Choudhary Shyam; Kumar, Avinash; Maheshkar, Sushila; Maheshkar, Vikas

doi:10.1007/s11042-018-5899-3

Cited by 36 publications

(17 citation statements)

References 54 publications

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“…Several techniques have been proposed that can independently resolve the problem of copy-move and splicing forgeries. However, very few techniques can detect both the forgeries in the same image [1,3]. Thus, developing a technique to identify forgery that meets day-to-day challenges is challenging.…”

Section: Related Workmentioning

confidence: 99%

“…However, owing to the variety of splicing, several approaches have been introduced in recent years. Prakash et al [1] extracted features through a block discrete cosine transform (BDCT) and enhanced threshold method. He et al [4] combined Markov features in a discrete wavelet transform (DWT) and discrete cosine transform (DCT) domain.…”

Section: Related Workmentioning

confidence: 99%

“…Further, Figure 10 shows the comparison of receiver operating characteristics (ROC) curves for CASIA v1.0, [8] 98.77 ---Lamba [10] 99.65 ---Prakash [1] 99 [8] 97.59 ---Lamba [10] 99.01 ---Prakash [1] 96 [22] 96.60 ---Muhammad [23] 96.39 ---Agarwal [6] 93.81 ---Alahmadi [3] 97.77 ---Proposed method 98.17 98.21 98.11 96.33 "-" indicates not calculated by the author observed that the ROC curve of the proposed algorithm is closer to the upper left corner, which depicts that it attains a better accuracy rate in comparison to the existing schemes.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Splicing is a tampering technique in which one or more than one part of a source image is duplicated and pasted into the target image. However, in CMF, some parts of the target image are duplicated and pasted into an equivalent image to cause misinterpretation or generate misleading results [1,2]. This paper is organized as follows: Section 2 discusses the existing splicing and copy-move procedures.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images. Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have been proposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously. However, a more efficient method is still in demand to meet the day-today challenges to detect these forgeries at the same time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binary pattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractional parameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively. Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and spliced images. Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas in the image. Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE, and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively. To validate the effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms of ROC, precision, recall, F1 score, F2 score, and accuracy. Moreover, the robustness of the proposed work is tested under rotation attack and better results are attained in comparison to the existing techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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“…Figure 1. Copy-move forgery example [1]: (a) original image and (b) copy-move image While many of the techniques mentioned above have advantages, they also have drawbacks, such as a large number of feature vectors and a high time complexity for example [3], [7], [12], [13]. This encourages us to look for ways to extract the features with the fewest features for each block while remaining resistant to post-processing operations.…”

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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Near duplicate detection of images with area and proposed pixel‐based feature extraction

Governor

Ramanujam²,

Mana

et al. 2022

Concurrency and Computation

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Efficient and accurate near-duplicate recognition is the trendy research area. Identification of invalid near-duplicate images offers a wide range of applications, including digital picture forensics, web-scale retrieval, and, social media analysis. This article intends to introduce a novel near duplicate detection model of images that consists of two stages such as (i) feature extraction and (ii) similarity computation. Originally, the image database is subjected to extracting the features, in which the area-based features and pixel-based features are extracted. Here, the area-based feature extraction includes the contrast context histogram (CCH-descriptors) and improved weighted bag of visual word (w-BovW) features; the pixel-based feature extraction includes the texture features like the proposed local vector pattern. Once the query image is given as the input, it is subjected to the feature extraction stage. Then, the feature vector database and the extracted features of query images are evaluated under similarity computation via improved Jaccard similarity evaluation. Thus, the near duplicate detection of the image is obtained in an effective manner. K E Y W O R D Sfeature extraction, feature vector database, local vector pattern, near duplicate detection, similarity computation INTRODUCTIONNowadays, digital contents are widespread and are redistributed easily, it may be lawful or unlawful. For instance, travelers, who travel to many places, take pictures, write travelogues and post them on the internet, these pictures are taken with or without viewpoint variation and that may be the shot from the same place. These images posted on the internet can be modified by other web users and they may repost them as their version.This generates a near-duplicate image. The near duplicate images are modified with the descriptions of normal images created through compression, geometric alterations, content augmentation, blurring, noise pollution, retaining and cutting out parts, and other techniques. The presence of those near-duplicate images causes wastage of storage, computing, and transmission resources, and it also affects the performance of the search engines critically, as the user has to search among the huge amount of near-duplicate images, for finding the image they want. Near-duplication of images 1,2 leads to wastage of resources in the network and it might be an indication of illegal transactions like image copyright violation. As a result, in web content security and image management, effective and efficient near-duplicate detection (NDD) of the picture is critical. Extracting useful features of an image to increase recognition accuracy is one of the key problems in near duplication image recognition. 3,4 Another issue is to increase the detection efficiency, which is difficult while considering the large image databases.The development of efficient and accurate NDD algorithms for various types of material such as video, image, text, and speech is a hot issue in research. 5,6 Near duplicates plays a crucial...

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An integrated method of copy-move and splicing for image forgery detection

Cited by 36 publications

References 54 publications

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Forgery detection algorithm based on texture features

Near duplicate detection of images with area and proposed pixel‐based feature extraction

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