Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Zhou, Guoshuai; Tian, Xin; Zhou, Aoying

doi:10.1007/s11704-021-0450-5

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…As a result of these physiology-inspired neural networks’ outstanding ability to extract dynamic information inside multi-dimensional signals, they have been widely used in numerous fields. Instances include feature extraction [ 27 ], pulse shape discrimination [ 28 , 29 , 30 ], image encryption [ 31 ], and image segmentation and fusion [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Multimodal Image Fusion for X-ray Grating Interferometry

Liu

Jiang

et al. 2023

Sensors

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X-ray grating interferometry (XGI) can provide multiple image modalities. It does so by utilizing three different contrast mechanisms—attenuation, refraction (differential phase-shift), and scattering (dark-field)—in a single dataset. Combining all three imaging modalities could create new opportunities for the characterization of material structure features that conventional attenuation-based methods are unable probe. In this study, we proposed an image fusion scheme based on the non-subsampled contourlet transform and spiking cortical model (NSCT-SCM) to combine the tri-contrast images retrieved from XGI. It incorporated three main steps: (i) image denoising based on Wiener filtering, (ii) the NSCT-SCM tri-contrast fusion algorithm, and (iii) image enhancement using contrast-limited adaptive histogram equalization, adaptive sharpening, and gamma correction. The tri-contrast images of the frog toes were used to validate the proposed approach. Moreover, the proposed method was compared with three other image fusion methods by several figures of merit. The experimental evaluation results highlighted the efficiency and robustness of the proposed scheme, with less noise, higher contrast, more information, and better details.

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Section: Methodsmentioning

confidence: 99%

Multimodal Image Fusion for X-ray Grating Interferometry

Liu

Jiang

et al. 2023

Sensors

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“…The authors used SVM to determine if a picture was genuine or fake. By merging the enhanced pulse coupled neural network (PCNN) with self-selected sub-images, Zhou et al in [35] were the first who present an image copy-move forgery passive detection approach. Zhu et al [36] created a Gaussian scale space for each scale space, retrieved the oriented FAST key points and ORB features in each scale space, utilized hamming distance to compare features, and then used random sample consensus (RANSAC) to remove mismatches.…”

Section: Related Workmentioning

confidence: 99%

Comparison of specific segmentation methods used for copy move detection

Ahmed

Alzaqebah

Jawarneh

et al. 2023

IJECE

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<p><span lang="EN-US">In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.</span></p>

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“…Zhou, Guoshua et.al (2022) became the first to introduce an image copy-move forgery passive detection method by combining the improved Pulse Coupled Neural Network (PCNN) and the self-selected sub-images [26]. The dual feature matching is used to match the features and locate the forgery regions.…”

Section: Brisk>surf>sift>akaze>kaze IImentioning

confidence: 99%

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

Singh

2023

Preprint

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With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. Copy Move Forgery is one such technique which is applied in conjunction with various other techniques to find forged regions within an image. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-Occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

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Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Cited by 6 publications

References 39 publications

Multimodal Image Fusion for X-ray Grating Interferometry

Multimodal Image Fusion for X-ray Grating Interferometry

Comparison of specific segmentation methods used for copy move detection

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

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