A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering

Bilal, Muhammad; Habib, Hafiz Adnan; Mehmood, Zahid; Yousaf, Rehan; Saba, Tanzila

doi:10.1080/00450618.2020.1715479

Cited by 31 publications

(26 citation statements)

References 36 publications

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“…Bag of Words (BoW), is also named Bag‐of‐feature (BoF), a feature descriptor, works via SURF, SIFT, and ORB. The extraction of ORB feature points with BoW is like SURF and SIFT but efficient and robust than SURF or SIFT (Baig et al, 2020; Bilal et al, 2020; Panchal, Panchal, & Shah, 2013). The ORB feature descriptor's calculation mechanism is simple enough that uses two bits and performs bitwise XOR operation to compute the Hamming distance between binary numbers and then the acquired results are counted.…”

Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law‐enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag‐of‐Words model to extract distinct and robust features from the iris image, followed by ensemble multi‐class‐SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi‐class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA‐v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

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Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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“…In [17], [22], [23], [55], [69] particle swarm optimization (PSO) algorithm is utilized to generate customized parameter values for each image. Several works such as [1], [4], [56], [66], [70], [71] increase the entire image contrast or resolution instead of decreasing the contrast threshold. In [70] single image super resolution algorithm is utilized to increase the image resolution.…”

Section: Dealing With Small or Smooth Cloned Regionsmentioning

confidence: 99%

“…In [1], [56], [66] the contrast limited adaptive histogram equalization algorithm is utilized to increase the image contrast. Similarly in [71] the dynamic histogram equalization method is utilized.…”

Section: Dealing With Small or Smooth Cloned Regionsmentioning

confidence: 99%

“…The way of localizing the keypoints varies from smooth regions to texture regions to make keypoints uniformly covering the entire image. Other works such as [5], [71] process specific regions within the image to extract more keypoints and more matched pairs. In [5] any suspicious region that contains insufficient number of matched keypoints is up-sampled and re-examined.…”

Section: Dealing With Small or Smooth Cloned Regionsmentioning

confidence: 99%

“…In [5] any suspicious region that contains insufficient number of matched keypoints is up-sampled and re-examined. In [71] matched keypoints are grouped into regions. The obtained regions are scaled up instead of scaling up the entire image.…”

Section: Dealing With Small or Smooth Cloned Regionsmentioning

confidence: 99%

See 2 more Smart Citations

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Zedan¹,

Soliman²,

Elsayed³

et al. 2021

IJACSA

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Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective.We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability . We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers' as it master the recent trends of CMFD and outline some future research directions.

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Advanced copy-move forgery detection: utilizing AKAZE in conjunction with SIFT algorithm for image forensics

Deb,

Kar,

Hassan

et al. 2024

Microsyst Technol

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A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering

Cited by 31 publications

References 36 publications

Light microscopic iris classification using ensemble multi‐class support vector machine

Light microscopic iris classification using ensemble multi‐class support vector machine

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Advanced copy-move forgery detection: utilizing AKAZE in conjunction with SIFT algorithm for image forensics

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