Detection of copy-move image forgery based on discrete cosine transform

Alkawaz, Mohammed Hazim; Sulong, Ghazali; Saba, Tanzila; Rehman, Amjad

doi:10.1007/s00521-016-2663-3

Cited by 118 publications

(55 citation statements)

References 40 publications

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“…• Phase #2: In this phase of experiments, our attention is on to assess the robustness of proposed method against various post-processing attacks. • Phase #3: In this phase of experiments, we concentrated on comparative analysis of proposed method with existing methods reported in the literature [3,4,7,12,16,[19][20][21][22].…”

Section: Experimentation Results and Discussionmentioning

confidence: 99%

“…It works well against blur, noise and JPEG attacks but cannot withstand rotation and scale transforms. In order to address the problem of rotated or flipped copied portions, Leida Li et al proposed a method [16] [19] concentrated on determining the block size on the detection rate. In [20], Toqeer Mahmood et al proposed a CMF method, explored DCT on overlapping blocks and PCA is employed for dimensionality reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Suresh¹,

Rao²

2019

IJIES

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In this digital age, the extensive usage of digital devices and availability of open source image editing software leads to the easy manipulation of digital images. Copy-Move Forgery (CMF) is a guileless and widespread approach to hide or duplicate a certain portion of the image without leaving visual clues. Thus, it is difficult to detect the copy-move forgeries and there is a need for forensic experts to rely on an effective approach for CMF detection for forensic analysis. Hence, an efficient passive block based approach to detect and localize CMF is proposed. In the proposed method, texture features are extracted from Differential Excitation Component on the overlapping blocks of the image. Similarity measure is performed for block matching and mapping is done to identify the duplicated regions. Evaluation is performed qualitatively and quantitatively on CoMoFoD dataset; true detection rate of 0.99 and false detection rate of 0.08 has been achieved. Evaluation validates that proposed method out performs the other existing methods with regard to detection accuracy.

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Section: Experimentation Results and Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Suresh¹,

Rao²

2019

IJIES

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“…To remove noise, a Gaussian filter is used by smoothing the image. The contrast enhancement technique of histogram equalization enhances the brightness of an image by normalizing the image intensity distribution whereas the technique of unsharp masking uses the blurred negative image for edge enhancement (Alkawaz, Sulong, Saba, & Rehman, ; Fadhil, Alkawaz, Rehman, & Saba, ; Waheed, Alkawaz, Rehman, Almazyad, & Saba, ). Authors proposed technique is tested to segment and classify related tasks on different medical image sets.…”

Section: Proposed Methodologymentioning

confidence: 99%

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Iqbal

Khan

Saba

et al. 2019

Microscopy Res & Technique

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Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

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“…Watermarking techniques insert some data about the regulations of using and copying a digital data. These rules limit the number of copies or ban any copying, depending on the statements of the watermark .…”

Section: Watermarking Applicationsmentioning

confidence: 99%

Concise analysis of current text automation and watermarking approaches

Alkawaz

Sulong

Saba

et al. 2016

Security Comm Networks

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With ceaseless utilization of web and other online advances, it has turned out to be amazingly simple to imitate, discuss, and convey digital material. Subsequently, confirmation and copyright assurance issues have been emerged. Text is the most widely used media of communication on the web as compared with images and videos. The significant part of books, daily papers, websites, commercial, research papers, reports, and numerous different archives are just the plain text. Therefore, copyrights protection of plain text is a critical issue that could not be accepted at all. This paper presents a concise analysis on information hiding techniques and their consequences. Various watermarking systems properties are highlighted along with different types of attacks and their possible defenses. Additionally, the current applications for text document watermarking and the reasons for the problems of text document watermarking are described. Finally, attacks characteristics are discussed including types of attacks, volumes, and nature on text. Current watermarking approaches are reviewed in depth, and consequently, their advantages and weaknesses are highlighted.

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Detection of copy-move image forgery based on discrete cosine transform

Cited by 118 publications

References 40 publications

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Concise analysis of current text automation and watermarking approaches

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