Results 251 to 260 of about 81,144 (285)

Copy-move forgery detection based on deep learning

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017
Copy-move forgery detection (CMFD) is probably one of the most active research areas within the blind image forensics field. Among existing algorithms, most of them are based on block and key-point methods, or combination of them. Recently, some deep convolutional neural networks methods have been applied in the image classification, image forensic ...
Junlin Ouyang, Yizhi Liu, Miao Liao
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Efficient Dense-Field Copy–Move Forgery Detection

IEEE Transactions on Information Forensics and Security, 2015
We propose a new algorithm for the accurate detection and localization of copy–move forgeries, based on rotation-invariant features computed densely on the image. Dense-field techniques proposed in the literature guarantee a superior performance with respect to their keypoint-based counterparts, at the price of a much higher processing time, mostly due
COZZOLINO, DAVIDE, POGGI, GIOVANNI, VERDOLIVA, LUISA   +2 more
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Copy-move forgery detection from printed images

SPIE Proceedings, 2014
Counterfeiting digital images through a copy-move forgery is one of the most common ways of manipulating the semantic content of a picture, whereby a portion of the image is copy-pasted elsewhere into the same image. It could happen, however, instead of a digital image only its analog version may be available. Scanned or recaptured (by a digital camera)
Irene Amerini, Roberto Caldelli, Alberto Del Bimbo, Andrea Di Fuccia, Luigi Saravo, Anna Paola Rizzo   +5 more
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A review on copy move image forgery detection techniques

2014 IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14), 2014
With the advent of powerful image editing tools, manipulating images and changing their content is becoming a trivial task. It is now possible to add, modify, or remove important features from an image without leaving any perceptual traces of tampering.
Muhammad Ali Qureshi, Mohamed A. Deriche
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A Non-intrusive Method for Copy-Move Forgery Detection

2011
The issue of verifying the authenticity and integrity of digital images is becoming increasingly important. Copy-move forgery is one type of image tempering that is commonly used for manipulating digital content; in this case, some part of an image is copied and pasted on another region of the image. Using a non-intrusive approach to solve this problem
Najah Muhammad, Muhammad Hussain 0001, Muhammad Ghulam, George Bebis   +3 more
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Copy-move detection of audio recording with pitch similarity

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
The widespread availability of audio editing software has made it very easy to create forgeries without perceptual trace. Copy-move is one of popular audio forgeries. It is very important to identify audio recording with duplicated segments. However, copy-move detection in digital audio with sample by sample comparison is invalid due to post-processing
Qi Yan 0004, Rui Yang 0006, Jiwu Huang
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Survey on image copy-move forgery detection

Multimedia Tools and Applications, 2023
Mayank Verma, Durgesh Singh 0001
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Copy-move forgery detection based on scaled ORB

Multimedia Tools and Applications, 2015
To solve the problem of the false matching and low robustness in detecting copy-move forgeries, a new method was proposed in this study. It involves the following steps: first, establish a Gaussian scale space; second, extract the orientated FAST key points and the ORB features in each scale space; thirdly, revert the coordinates of the orientated FAST
Ye Zhu, Xuanjing Shen, Haipeng Chen 0002
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A Comparative Analysis of Copy-Move Forgery Detection Algorithms

International Journal of Electronic Security and Digital Forensics, 2022
Mohassin Ahmad, Farida Khursheed
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Copy move forgery detection

International Conference & Workshop on Electronics & Telecommunication Engineering (ICWET 2016), 2016
A.B. Patankar, A. Sukhpal, S. Shetye, A. ShroffShroff   +3 more
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