Image Splicing Detection Based on Markov Features in QDCT Domain

Li, Ce; Ma, Qiang; Xiao, Lei; Li, Ming; Zhang, Aihua

doi:10.1007/978-3-319-22186-1_17

Cited by 13 publications

(23 citation statements)

References 8 publications

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“…Li et al (2017) [ 16 ] applied the Markov in quaternion discrete cosine transform (QDCT) to detect image splicing by capturing the inter-block correlation between the QDCT coefficients.…”

Section: Methodsmentioning

confidence: 99%

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Jalab

Subramaniam

Ibrahim

et al. 2019

Entropy

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Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.

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“…Li et al (2017) [ 16 ] applied the Markov in quaternion discrete cosine transform (QDCT) to detect image splicing by capturing the inter-block correlation between the QDCT coefficients.…”

Section: Methodsmentioning

confidence: 99%

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Jalab

Subramaniam

Ibrahim

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…In [31], an excellent method to highlight statistical changes from Markov's transition probability characteristics was introduced. Other authors have proposed combinations to improve results, such as Markov-DCT-DWT [32], Markov-DCT [33], Markov-QDCT [34], and Markov-Octonio DCT [35]. As copy-move detection, a deep learning approach is used for splicing detection.…”

Section: Splicing Detectionmentioning

confidence: 99%

Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Osorio

Robayo

2021

Rev.Fac.Ing.Univ.Antioquia

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Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size.

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“…To verify the feasibility of using cooperation promotion solution (CP), we first simulate and analyze the changing trend of requester's payment and the variation trend of quality of service. Secondly, we compare the accuracy of cos-evaluation algorithm (CE) with Markov-GM model [41], ARMA model, and then we compare the requester's payoff in CP, ALLD, ALLC, CToD, and TFT. To check the validity of quality of service improvement solution (QI), we use Facebook social network [42] and auction dataset [43] to simulate the interaction process between the platform and workers.…”

Section: A Experiments Settingmentioning

confidence: 99%

Two-Stage Game Design of Payoff Decision-Making Scheme for Crowdsourcing Dilemmas

Xia

Zhang

Cheng

et al. 2020

IEEE/ACM Trans. Networking

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Crowdsourcing uses collective intelligence to finish complicated tasks and is widely applied in many fields. However, the crowdsourcing dilemmas between the task requester and the task completer restrict the efficiency of system severely, e.g., the cooperation dilemma leads to the failure in the interactions and the quality of service dilemma results in the inability of task completer to provide high-quality service. Current research usually focuses on solving only one aforementioned dilemma and fails to integrate perfectly with the service architectural pattern of crowdsourcing systems. In this article, combined with the crowdsourcing interaction phase, we limit the objects that cause dilemma and propose a two-stage game payoff decision-making scheme (TGPD) to overcome these shortcomings. To solve the cooperation dilemma between the requester and the crowdsourcing platform, we first propose a dynamic payment method based on the reputation-quality rules for the task requester, and then develop a cos-evaluation algorithm to estimate platform's cost, last design a co-determine algorithm to determine whether the platform adopts a cooperative strategy. To address the quality of service dilemma between the crowdsourcing platform and the workers, we first present an auction-screening method to estimate the reasonable recruitment range of workers which can be optimized by the result of cos-evaluation algorithm, and then use a reward distribution method to motivate workers to complete tasks with high quality and on time. The experimental results indicate that our new scheme successfully increases the worker's and platforms' payoffs at the same time, improves the accuracy of screening workers, enhances the worker's quality of service, and decreases the platform's cost.

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Image Splicing Detection Based on Markov Features in QDCT Domain

Cited by 13 publications

References 8 publications

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Two-Stage Game Design of Payoff Decision-Making Scheme for Crowdsourcing Dilemmas

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