A new approach for detecting copy-move forgery in digital images

Shabanian, Hanieh; Mashhadi, Farshad

doi:10.1109/wnyipw.2017.8356252

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…The first process is decomposition with Gaussian pyramid decomposition. This method will reduce image size by ½ times and smooth it with a Gaussian kernel (Shabanian & Mashhadi, 2018). Then, the images will be put to the layer above the previous image.…”

Section: Gaussian Pyramid Decompositionmentioning

confidence: 99%

“…However, this combination is slowest than SIFT only (Sun et al, 2018;Zheng et al, 2016). On the other hand, the Gaussian pyramid is helpful to detect copy-move forgery, including the fake image with noise and JPEG Compression, without burdening the testing time (Shabanian & Mashhadi, 2018). Shabanian and Mashhadi (2018) use Gaussian pyramid decomposition in this research field.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the Gaussian pyramid is helpful to detect copy-move forgery, including the fake image with noise and JPEG Compression, without burdening the testing time (Shabanian & Mashhadi, 2018). Shabanian and Mashhadi (2018) use Gaussian pyramid decomposition in this research field. They use it to resize the forged image before dividing it into blocks.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 15 Effect in Gaussian Pyramid in detection result in different size (in pixels); Left to right : 512 × 512, 256 × 256, 128 × 128 and 64 × 64(Shabanian & Mashhadi, 2018) …”

mentioning

confidence: 99%

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Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms

Rahma

Utami

2022

telematika.

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One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.

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Section: Gaussian Pyramid Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Figure 15 Effect in Gaussian Pyramid in detection result in different size (in pixels); Left to right : 512 × 512, 256 × 256, 128 × 128 and 64 × 64(Shabanian & Mashhadi, 2018) …”

mentioning

confidence: 99%

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Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms

Rahma

Utami

2022

telematika.

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“…Such multi-resolution representations are useful for reducing the computational complexity of algorithms and for accessing objects or elements in a scene at various scales. Multi-resolution techniques are widely used in various imaging and computational applications such as image segmentation [13], image manipulation [14], motion analysis [15], and for stereo depth estimation [16].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm

Shabanian¹,

Balasubramanian²

2022

Preprint

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A dense depth-map of a scene at an arbitrary view orientation can be estimated from dense view correspondences among multiple lower-dimensional views of the scene. These low-dimensional view correspondences are dependent on the geometrical relationship among the views and the scene. Determining dense view correspondences is difficult in part due to presence of homogeneous regions in the scene and due to presence of occluded regions and illumination differences among the views. We present a new multi-resolution factor graph-based stereo matching algorithm (MR-FGS) that utilizes both intra-and inter-resolution dependencies among the views as well as among the disparity estimates. The proposed framework allows exchange of information among multiple resolutions of the correspondence problem and is useful for handling larger homogeneous regions in a scene. The MR-FGS algorithm was evaluated qualitatively and quantitatively using stereo pairs in the Middlebury stereo benchmark dataset based on commonly used performance measures. When compared to a recently developed factor graph model (FGS), the MR-FGS algorithm provided more accurate disparity estimates without requiring the commonly used post-processing procedure known as the left-right consistency check. The multi-resolution dependency constraint within the factor-graph model significantly improved contrast along depth boundaries in the MR-FGS generated disparity maps.

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