Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion

Han, Jong Goo; Park, Tae Hee; Moon, Yuseok; Eom, Il Kyu

doi:10.1117/1.jei.25.2.023031

Cited by 44 publications

(14 citation statements)

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“…Hence, a better imperceptibilty factor is decoded in the proposed system where the correct and reliable identification is carried out using simple techniques. Apart from this, the outcome of the proposed study is compared with the existing approaches related to localization-based [32] and feature-extraction based [33]. Using the same dataset, the comparative analysis is carried out.…”

Section: Results Analysismentioning

confidence: 99%

“…The prime reason behind this is that localization-based approach [32] performs identification of the traces equivalent to the proposed system but it does so with more number of the attributes associated with it and hence proposed system offers better optimization performance. The feature-extraction based approach [33] make use of random theory along with usage of thresholding however that do it recursively that significantly degrades the accuracy level in contrast to proposed system.…”

Section: Results Analysismentioning

confidence: 99%

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Novel framework for optimized digital forensic for mitigating complex image attacks

Shashidhar¹,

Ramesh²

2020

IJECE

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Digital Image Forensic is significantly becoming popular owing to the increasing usage of the images as a media of information propagation. However, owing to the presence of various image editing tools and softwares, there is also an increasing threats over image content security. Reviewing the existing approaches of identify the traces or artifacts states that there is a large scope of optimization to be implmentation to further enhance teh processing. Therfore, this paper presents a novel framework that performs cost effective optmization of digital forensic tehnqiue with an idea of accurately localizing teh area of tampering as well as offers a capability to mitigate the attacks of various form. The study outcome shows that propsoed system offers better outcome in contrast to existing system to a significant scale to prove that minor novelty in design attribute could induce better improvement with respect to accuracy as well as resilience toward all potential image threats.

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Section: Results Analysismentioning

confidence: 99%

Section: Results Analysismentioning

confidence: 99%

Novel framework for optimized digital forensic for mitigating complex image attacks

Shashidhar¹,

Ramesh²

2020

IJECE

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show abstract

“…The method gives the best accuracy on the image's chrominance channel using a Support Vector Machine (SVM) classifier with 10-fold cross-validation. Markov feature extraction [27] for color images is proposed using threshold expansion and maximization. Again, Markov-based features [28] from two different domains are used to distinguish forged and authentic images.…”

Section: A Conventional Methodsmentioning

confidence: 99%

Fusion of Handcrafted and Deep Features for Forgery Detection in Digital Images

Walia

Kumar

et al. 2021

IEEE Access

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Content authentication of digital images has captured the attention of forensic experts and security researchers due to a multi-fold increase in the dissemination of multimedia data through the open and vulnerable Internet. Shrewd attackers successfully devise novel ways to challenge state-of-art forensic tools used for forgery detection in digital images. Feature engineering approaches have yielded up to 97% accuracy on benchmarked datasets. Deep learning approaches have shown promising results in various image classification problems but cannot find hidden patterns in digital images, which can reliably detect image forgeries. State-of-art accuracy of deep learning approaches for forgery detection is up to 98% on benchmarked datasets. The objective of the proposed approach is to further escalate the detection accuracy, pushing it near 100%.In this paper, a synergy of handcrafted features based on color characteristics and deep features using the 'image's luminance channel is employed to mine patterns responsible for accurate forgery detection. In the first Stream, 648-D Markov-based features are computed from the quaternion discrete cosine transform of the image. In the second Stream, the luminance channel of YCbCr colorspace is used to extract the Local Binary Pattern of the image. Further, local binary feature maps are fed to the pre-trained ResNet-18 model to obtain a 512-D feature vector called 'ResFeats' from the last layer of the 'model's convolutional base portion. The handcrafted features from Stream I and ResFeats from Stream II are combined to form an 1160-D feature vector. Further, classification is performed using a shallow neural network, and the method is tested on CASIA v1 and CASIA v2 datasets. The accuracy of the proposed fusion-based approach is 99.3% on benchmark datasets. INDEX TERMS Image forensics; image manipulations; pre-trained networks; Markov process; local binary pattern; deep learning; machine learning.

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“…J.G. Han, Park, Moon and Eom (2016) applied Markov features and expectationmaximization (EM) technique while Q. Zhang, W. Lu and Weng (2016) combined DCT and counterlet. B. Chen, Qi, X.…”

Section: Digital Signal Processing Techniques and Machine Learning Cl...mentioning

confidence: 99%

MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection

Gokhale¹,

Pramod²,

Thepade³

et al. 2021

Preprint

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In this paper we propose a novel socio-inspired convolutional neural network (CNN) deep learning model for image splicing detection. Based on the premise that learning from the detection of coarsely spliced image regions can improve the detection of visually imperceptible finely spliced image forgeries, the proposed model referred to as, MissMarple, is a twin CNN network involving feature-transfer learning. Results obtained from training and testing the proposed model using the benchmark datasets like Columbia splicing, WildWeb, DSO1 and a proposed dataset titled AbhAS consisting of realistic splicing forgeries revealed improvement in detection accuracy over the existing deep learning models.

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Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion

Cited by 44 publications

References 0 publications

Novel framework for optimized digital forensic for mitigating complex image attacks

Novel framework for optimized digital forensic for mitigating complex image attacks

Fusion of Handcrafted and Deep Features for Forgery Detection in Digital Images

MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection

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