Journal ArticleDOI
Digital image forgery detection using passive techniques: A survey
TLDR
An attempt is made to survey the recent developments in the field of digital image forgery detection and a complete bibliography is presented on blind methods for forgery Detection.About:
This article is published in Digital Investigation.The article was published on 2013-10-01. It has received 347 citations till now. The article focuses on the topics: Digital image & Authentication.read more
Citations
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Journal ArticleDOI
Digital image splicing detection based on Markov features in DCT and DWT domain
TL;DR: A Markov based approach is proposed to detect image splicing and can outperform some state-of-the-art methods, making the computational cost more manageable.
Journal ArticleDOI
Detecting GAN generated Fake Images using Co-occurrence Matrices.
Lakshmanan Nataraj,Tajuddin Manhar Mohammed,B.S. Manjunath,Shivkumar Chandrasekaran,Arjuna Flenner,Jawadul H. Bappy,Amit K. Roy-Chowdhury +6 more
TL;DR: A novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning, which achieves more than 99% classification accuracy in both datasets.
Book ChapterDOI
BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization
TL;DR: B BusterNet is a pure, end-to-end trainable, deep neural network solution that outperforms state-of-the-art copy-move detection algorithms by a large margin on the two publicly available datasets, CASIA and CoMoFoD, and that is robust against various known attacks.
Journal ArticleDOI
Copy-move forgery detection
Nor Bakiah Abd Warif,Ainuddin Wahid Abdul Wahab,Mohd Yamani Idna Idris,Roziana Ramli,Rosli Salleh,Shahaboddin Shamshirband,Kim-Kwang Raymond Choo +6 more
TL;DR: The common CMFD workflow of feature extraction and matching process using block or keypoint-based approaches is characterized, and the types of copied regions are categorized.
Journal ArticleDOI
A bibliography of pixel-based blind image forgery detection techniques
TL;DR: A comprehensive survey of different forgery detection techniques is provided, complementing the limitations of existing reviews in the literature and covering image copy-move forgery, splicing, forgery due to resampling, and the newly introduced class of algorithms, namely image retouching.
References
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Proceedings ArticleDOI
Blind Image Restoration Using a Block-Stationary Signal Model
Tom E. Bishop,James R. Hopgood +1 more
TL;DR: A novel method for blind image restoration which is a multidimensional extension of an approach used successfully for audio restoration, and a maximum marginalised a posteriori (MMAP) blur estimate is obtained by optimising the resulting probability density function.
Detection of Copy-Move Forgery in Digital Images
TL;DR: This paper investigates the problem of detecting the copy-move forgery and describes an efficient and reliable detection method that may successfully detect the forged part even when the copied area is enhanced/retouched to merge it with the background and when the forged image is saved in a lossy format, such as JPEG.
Journal ArticleDOI
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
TL;DR: The problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward.
Journal ArticleDOI
Determining Image Origin and Integrity Using Sensor Noise
TL;DR: A unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors is provided.