Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Zheng, Jiangbin; Liu, Yanan; Ren, Jinchang; Zhu, Tingting; Yan, Yijun; Yang, Heng

doi:10.1007/s11045-016-0416-1

Cited by 83 publications

(30 citation statements)

References 39 publications

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“…Zernike moments have a wide range of applications in different fields such as image recognition and classification [20,25,27,28], copy-move-forgery detection [29][30][31][32][33], video-forgery detection [34], watermark detection [35][36][37], and medical-image retrieval [38]. In the copy-move forgery-detection problem, Zernike moment features-based methods notably showed impressive performances to different kinds of transformations in comparison with other approaches [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

“…In the copy-move forgery-detection problem, Zernike moment features-based methods notably showed impressive performances to different kinds of transformations in comparison with other approaches [29][30][31]. On the other hand, SIFT features were used extensively in image-retrieval studies [39][40][41][42][43] and copy-move-forgery detection [32,44,45]. A coupled multi-index method was proposed in Reference [46] to exploit the feature fusion of local color feature and SIFT feature where both kinds of descriptors are extracted for all keypoints.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Image-Retrieval Method for Image-Splicing Validation

et al. 2019

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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hybrid Image-Retrieval Method for Image-Splicing Validation

et al. 2019

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“…The block-based approach uniformly partitions the image into blocks, this blocks can either be a smaller nonoverlapping or overlapping square shape or partitions of circular shapes [17]. While in the keypoint-based approach, there is no subdivision rather it computes its feature on image portion that has a high entropy [18]. Table 1 shows a summary of the two approaches.…”

Section: Copy-move Forgery Detection Approachmentioning

confidence: 99%

Hybridized Technique for Copy-Move Forgery Detection Using Discrete Cosine Transform and Speeded-Up Robust Feature Techniques

Ojeniyi¹,

Adedayo²,

Ismaila³

et al. 2018

IJIGSP

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As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing tools have become easily available on the internet, which has made people who are a novice in the field of image editing, to be capable of tampering with an image easily without leaving any visible clue or trace behind, which has led to increase in digital images losing authenticity. This has led to developing various techniques for tackling authenticity and integrity of forged images. In this paper, a robust and enhanced algorithm is been developed in detecting copy-move forgery, which is done by hybridizing block-based DCT (Discrete Cosine Transform) technique and a keypointbased SURF (Speeded-Up Robust Feature)technique using the MATLAB platform. The performance of the above technique has been compared with DCT and SURF techniques as well as other hybridized techniques in terms of precision, recall, FPR and accuracy metrics using MICC-F220 dataset. This technique works by applying DCT to the forged image, with the main goal of enhancing the detection rate of such image and then SURF is applied to the resulting image with the main goal of detecting those areas that are been tampered with on the image. It has been observed that this paper's technique named HDS has an effective detection rate on the MICC-F220 dataset with multiple cloning attacks and other various attacks such as rotation, scaling, a combination of scaling plus rotation, blur, compression, and noise.

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“…Pun et al [18] integrated both the block-based and keypoint-based methods to detect the forged regions. Several keypointbased methods involved with segmentation methods have been reported in the following references: [19][20][21][24][25][26][27]. Christlein et al [28] evaluated the performance of feature sets in existing copy-move forgery detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

Huang

Ciou

2019

J Image Video Proc.

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The increasing popularity of the internet suggests that digital multimedia has become easier to transmit and acquire more rapidly. This also means that this multimedia has become more susceptible to tampering through forgery. One type of forgery, known as copy-move duplication, is a specified type that usually involves image tampering. In this study, a keypoint-based image forensics approach based on a superpixel segmentation algorithm and Helmert transformation has been proposed. The purpose of this approach is to detect copy-move forgery images and to obtain forensic information. The procedure of the proposed approach consists of the following phases. First, we extract the keypoints and their descriptors by using a scale-invariant feature transform (SIFT) algorithm. Then, based on the descriptor, matching pairs will be obtained by calculating the similarity between keypoints. Next, we will group these matching pairs based on spatial distance and geometric constraints via Helmert transformation to obtain the coarse forgery regions. Then, we refine these coarse forgery regions and remove mistakes or isolated areas. Finally, the forgery regions can be localized more precisely. Our proposed approach is a more robust solution for scaling, rotation, and compression forgeries. The experimental results obtained from testing different datasets demonstrate that the proposed method can obtain impressive precision/ recall rates in comparison to state-of-the-art methods.

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Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Cited by 83 publications

References 39 publications

Hybrid Image-Retrieval Method for Image-Splicing Validation

Hybrid Image-Retrieval Method for Image-Splicing Validation

Hybridized Technique for Copy-Move Forgery Detection Using Discrete Cosine Transform and Speeded-Up Robust Feature Techniques

Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

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