Nonoverlapping Blocks Based Copy-Move Forgery Detection

Sun, Yu; Ni, Rongrong; Zhao, Yao

doi:10.1155/2018/1301290

Cited by 27 publications

(12 citation statements)

References 22 publications

(32 reference statements)

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“…At the outset, an input image is converted into the YCbCr color channel. Then, for each image, the local binary pattern is calculated and transformed in the frequency domain using DFrCT to capture [15] 79.20 88.40 83.54 80.88 Emam [19] 87.50 92.70 90.02 88.49 Yang [12] 78.61 90.27 84.04 80.69 Sun [9] 83.33 90.91 86.96 84.74 Li [14] 100 -98.97 -Prakash [13] 87 Table 4. Comparison of computational load on CASIA v1.0 dataset…”

Section: Resultsmentioning

confidence: 99%

“…Most of the block-based methods are based on DCT, zernike moments, DWT, and discrete fractional wavelet transform (DFrWT) [9,10] for extracting features. In contrast, keypoint-based techniques include scale invariant feature transform (SIFT), speeded up robust features (SURF) and, oriented FAST and rotated BRIEF (ORB) [9,[11][12][13][14][15]. Christlein et al [16] examined the detection performance of various methods; among them, zernike moments proved to be efficient owing to its small memory.…”

Section: Related Workmentioning

confidence: 99%

“…Further, Table 3 depicts the comparative analysis of the proposed algorithm with other algorithms [9,[11][12][13][14][15][16][17][18][19][20] on GRIP, COVERAGE, and IMD datasets. For the IMD dataset, the sensitivity of 100%, precision of 98%, F 1 score of 98.99%, and 99.59% F 2 score are achieved.…”

Section: Comparative Analysismentioning

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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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Large sized kernels can overlook finer details and skip essential information; on the contrary, very small sized kernels can provide too much information which can sometimes be misleading. In detection of copy move forgery attacks, lot of methods uses block-based approaches [21,37] and look for similarities between the feature vectors generated by each block. The block size in that scenario is analogous to the receptive field of the CNN.…”

Section: Experimental Analysismentioning

confidence: 99%

Dual branch convolutional neural network for copy move forgery detection

Goel

Kaur

Bala

2020

IET Image Processing

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The advent of digital era has seen a rise in the cases of illegal copying, distribution and forging of images. Even the most secure data channels sometimes suffer to validate the integrity of images. Forgery of multimedia data is devastating in various important applications like defence and satellite. Increased illegal tampering of images has paved way for research in the area of digital forensics. Copy move forgery is one of the various tampering techniques which is used for manipulating an image's content. A deep learning–based passive Copy Move Forgery Detection algorithm is proposed that uses a novel dual branch convolutional neural network to classify images as original and forged. The dual branch convolutional neural network extracts multi‐scale features by employing different kernel sizes in each branch. Fusion of extracted multi‐scale features is then performed to achieve a good accuracy, precision and recall scores. Experiment analysis on MICC F‐2000 dataset has been performed under two different kernel size combinations. Extensive result analysis and comparative analysis proves the efficacy of proposed architecture over existing architecture in terms of performance scores, computation time, and complexity.

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“…Various block division and segmentation methods can be considered for use in preprocessing. An image can be divided into overlapping square blocks [9][10][11], non-overlapping square blocks [12], or circular blocks [13,14]. Image segmentation techniques [15,16] are often included in the preprocessing step to separate the copied source region from the pasted target region.…”

Section: Introductionmentioning

confidence: 99%

Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

et al. 2020

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Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.

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Nonoverlapping Blocks Based Copy-Move Forgery Detection

Cited by 27 publications

References 22 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

Dual branch convolutional neural network for copy move forgery detection

Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

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