A new keypoint-based copy-move forgery detection for small smooth regions

Wang, Xiangyang; Li, Shuo; Liu, Yunan; Niu, Ying; Yang, Heng; Zhou, Zhili

doi:10.1007/s11042-016-4140-5

Cited by 49 publications

(22 citation statements)

References 35 publications

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“…In addition, this method exploited a circular region instead of a square one to improve the performance in mirror reflection transformation. Wang et al [Wang, Li, Niu et al (2017)] presented a method based on local information entropy which divided each non-overlapping block into irregular superpixels, then they extracted the robust keypoints from each superpixel. Finally, they used exponential moments to construct local features of each keypoint.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Tan¹,

Wu²,

Wu³

et al. 2019

Journal of New Media

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Digital images can be tampered easily with simple image editing software tools. Therefore, image forensic investigation on the authenticity of digital images' content is increasingly important. Copy-move is one of the most common types of image forgeries. Thus, an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper. These methods are classified into three types: block-based methods, keypoint-based methods, and deep learning-based methods. In addition, the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost. Finally, further research directions are discussed.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Tan¹,

Wu²,

Wu³

et al. 2019

Journal of New Media

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“…Keypoint-based methods make up for the above shortcomings by extracting the keypoints in the high entropy regions and describing local features. Among them, Scale-Invariant Feature Transform (SIFT) [20]- [24], [29] and Speeded Up Robust Features (SURF) [25]- [27], [30] are widely used in the detection stage for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…SURF is used for fast detection and SIFT is used for robustness of CMFD. For detection of forgery in small smooth regions, a method is given in [27]. In this, the image is segmented into nonoverlapped and irregular super pixels.…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood Search Algorithm

2020

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Copy-move is one of the most commonly used methods of tampering with digital images. Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD). This paper proposes an efficient CMFD method via clustering SIFT keypoints and searching the similar neighborhoods to locate tampered regions. In the proposed method, the keypoints are clustered based on scale and color, grouped into several smaller clusters and matched separately, which reduce the high time complexity caused in matching caused by the high dimensionality of SIFT. In order to locate the tampered regions accurately at pixel level finally, a novel localization algorithm is designed to compare the similar neighborhoods of matching pairs by two similarity measures, and mark the tampered regions in pixels iteratively. We experimented on three different image data sets including kinds of tampering means to compare and verify the effectiveness and robustness of proposed method. The experimental results show that the proposed method is superior to existing state-of-art methods in terms of matching time complexity, detection reliability and forgery location accuracy. INDEX TERMS Copy-move forgery detection, digital image forensics, keypoint clustering, similar neighborhood search algorithm.

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“…In addition, most keypoint extraction methods executed with the default parameters cannot obtain sufficient keypoints in smooth tampered regions. Inspired by Reference [44], in this paper, the detector response thresholds, A-KAZE T and SU RF T , are set to small values to obtain sufficient interest points. As is depicted in Figure 4b, the image of the Japan tower is hidden by the sky region within the tampered same image.…”

Section: Feature Extractionmentioning

confidence: 99%

An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

2018

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The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

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A new keypoint-based copy-move forgery detection for small smooth regions

Cited by 49 publications

References 35 publications

A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood Search Algorithm

An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

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