Image Copy-Move Forgery Detection Based on SIFT and Gray Level

Shen, Xuan; Zhu, Ye; Lv, Yiliang; Chen, Haipeng

doi:10.4028/www.scientific.net/amm.263-266.3021

Cited by 6 publications

(9 citation statements)

References 7 publications

(6 reference statements)

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“…(b) represents the copy-move forgery image of (a). (c), (d), and (e) respectively represents the detection results of the algorithm in [9], [10] and this paper. The connected part of the figure is the detected copy-move source area and target area.…”

Section: Resultsmentioning

confidence: 99%

“…The three sets of images represent the detection results of copy-move, copy-scaling-move and copy-rotation & scaling-move forgery respectively. In order to illustrate the effectiveness of the proposed method, comparative experiments are conducted between the improved SURF algorithm and the algorithm in [9] and [10] in the same experimental environment. In Fig.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Copy-Move Forgery Detection Method for VDR Image

Hu¹,

Wang²,

Gao³

2017

dtcse

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Abstract. The authenticity of VDR images is facing a severe test due to advanced image processing technology. Aiming at the issue of using copy-move forgery method to cover up the truth of marine accidents in VDR images, a VDR image copy-move blind forensics method is proposed. In the proposed work, the images are decomposed by Bi-dimensional Empirical Mode Decomposition (BEMD), and SURF algorithm is employed to obtain feature point descriptor. Comparisons with the prevailing methods show that the proposed method improves the detection accuracy. Furthermore, it is invariant to any rotation or scaling in the copy-move region.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Copy-Move Forgery Detection Method for VDR Image

Hu¹,

Wang²,

Gao³

2017

dtcse

View full text Add to dashboard Cite

show abstract

“…For example, Shen et al's method [27] is robust against Gaussian blurring and its false matching rate is lower than that of conventional methods. However, their method has a high time complexity because the SIFT and grey level features must be extracted together.…”

Section: Keypoint-based Methodsmentioning

confidence: 99%

“…When compared with previous methods proposed by Fridrich et al [8] and Popescu et al [17], the computational time, true positive rate, and false positive rate of this SIFT-based method were all improved. In a related approach [27], the grey-level feature is combined with the SIFT features to form a new feature vector. This approach successfully reduced false matching rate, but at the expense of greater time complexity.…”

Section: Keypoint-based Methodsmentioning

confidence: 99%

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Sadeghi

Jalab

Wong

et al. 2017

MJCS

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With the development of powerful image processing tools and the increasing trend of using images as the main carrier of information, digital image forgery has become an increasingly serious issue. In copy-move forgery, one part of an image is copied and placed elsewhere in the same image. This paper puts forward an effective method based on SIFT for detecting copy-move forgery in digital image. The proposed method can accurately authenticate digital image and locate areas which have been tampered with. The algorithm starts by using scale-invariant features transform (SIFT) to extract local image features, which are known as keypoints, and then searches for similar keypoints based on their Euclidean distances. Finally, the matched keypoints, which represent the copied and pasted areas, are associated with one and another to indicate which parts of the image have been tampered with. Experiments are performed to validate the effectiveness of this method on different attacks, and to quantify its robustness against post-processing. Results show that the method is robust against several geometric processings, including JPEG compression, rotation, noise, and scaling. As a representative result, when considering the standard test dataset MICC-F220, the proposed method achieves true and false positive rates of 100% and 3.12%, respectively.

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“…These features are computed only on the image itself, without any division, and the extracted features vectors per keypoint are compared with each other to find similar keypoints. Two well-known keypoint-based methods are: Scale Invariant Transform Methods (SIFT) [31,32] and Speeded Up Robust Features (SURF) [33,34]. One of the state of art of keypoint based methods is (Amerini et al, 2011) [32] that proposed a novel method based on SIFT, which is able to examine region duplication forgery and image splicing.…”

Section: )mentioning

confidence: 99%

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Diaa¹,

Mohammed²

2017

ijacsa

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Abstract-Due to the availability of powerful image editing softwares, forgers can tamper the image content easily. There are various types of image forgery, such as image splicing and region duplication forgery. Region duplication is one of the most common manipulations used for tampering digital images. It is vital in image forensics to authenticate the digital image. In this paper, a novel region duplication forgery detection approach is proposed. By segmenting the input image based on the colour features, sufficient number of centroids are produced, that exist even in small or smooth regions. Then, the Least Significant Bit (LSB) of all the colours of pixels in each segment are extracted to build the signature vector. Finally, the hamming distance is calculated through exploiting the signature vector of image to find the dissimilarity. Various experimental results are provided to demonstrate the superior performance of the proposed scheme under some post processing operations such as scaling attack.

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Image Copy-Move Forgery Detection Based on SIFT and Gray Level

Cited by 6 publications

References 7 publications

A Copy-Move Forgery Detection Method for VDR Image

A Copy-Move Forgery Detection Method for VDR Image

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

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