New Copy-Move Forgery Detection Algorithm

Jaafar, Roqaya Hamad; Rasool, Zahraa. H.; Alasadi, Abbas H. Hassin

doi:10.1109/rusautocon.2019.8867813

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Each classification of the passive approach can be divided further. Image copy-move fabrication, retouching fabrication, and picture splicing falsification are the three types of image fraud that are classified according to the hardening process in the image [14].…”

Section: Passive Methodmentioning

confidence: 99%

Detection of Image Tempering: Conventional and Deep Learning-based Techniques

Da. Khallaf,

Hanon Alasadi1,2

2023

JEPS

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In light of the cumulative use of digital images in a wide range of apps, as well as the accessibility ofimage manipulation software, detecting image alteration has become a difficult task. The copy-movetechnique is the most commonly used sort of picture counterfeiting. A portion of an image is duplicatedand manipulated in various ways. Handcrafted qualities are commonly used in the identification ofpicture forgery and counterfeiting. It has always been a difficulty with earlier photo reproductiondetection systems that they would rather only detect a specific type of tampering if they are aware ofspecific image features. Deep learning is currently being used to detect image alteration, which is abreakthrough. These strategies were even more efficient than prior methods since they were able toextract complex images from them. The purpose of this work is to teach you about deep learning-basedpicture forgery detection methods, why they function, and how they can be improved upon. In addition,you will learn about publicly available image forgery datasets.

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Section: Passive Methodmentioning

confidence: 99%

Detection of Image Tempering: Conventional and Deep Learning-based Techniques

Da. Khallaf,

Hanon Alasadi1,2

2023

JEPS

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“…A Ciratefi-based method is used to localize the faked pixel. A novel technique was suggested by R. H. et al [3] to detect the copy-move forgery, which is the most common type of forgery attack. The detection and localization of forgeries is a notable issue that has drawn and continues to draw the attention of academics working in the area of digital based forensics, according to Pranshav Gajjar et al [4].…”

Section: Related Workmentioning

confidence: 99%

Detection of Copy-Move Image Forgery Using Local Binary Pattern from Detailed Wavelet Coefficient

Kaur,

Lala,

Kalsi

2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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One of the most prevalent types of image forgery is copy-move forgery. A portion of the image is being copied and further pasted to a different location inside the identical image during the copy-move approach in order to hide a significant portion of the image. Finding duplicate portions in the image is the purpose of the copy-move based forgery detection technique. In this paper, we suggest a system which tends to detects forged portion in a forgery image. The DILBP (Detailed image local binary pattern) approach is used in this work to extract features, which includes extraction of feature, matching of feature, duplicate valued block detection. Several experiments have been initiated on a forged image to detect copy-move forged part. The experimental conclusions highlight that the suggested system is efficient for quality with respect to accuracy and speed.

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