• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.527-534
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• 2019

Recently, Photo editing softwares such as digital cameras, Paintshop Pro, and Photoshop digital can create counterfeit images easily. Various techniques for detection of tamper images or forgery images have been proposed in the literature. A form of digital forgery is copy-move image forgery. Copy-move is one of the forgeries and is used wherever you need to cover a part of the image to add or remove information. Copy-move image forgery refers to copying a specific area of an image itself and pasting it into another area of the same image. The purpose of copy-move image forgery detection is to detect the same or very similar region image within the original image. In this paper, we proposed fast detection of forgery image using four step search based on discrete cosine transform and a four step search algorithm using discrete cosine transform (FSSDCT). The computational complexity of our algorithm reduced 34.23 % than conventional DCT three step search algorithm (DCTTSS).

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.47-57
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• 2019

We examine the weaknesses of the existing OOXML-based MS-Word file structure, and analyze how data concealment and forgery are performed in MS-Word digital documents. In case of forgery by including hidden information in MS-Word digital document, there is no difference in opening the file with the MS-Word Processor. However, the computer system may be malfunctioned by malware or shell code hidden in the digital document. If a malicious image file or ZIP file is hidden in the document by using the structural vulnerability of the MS-Word document, it may be infected by ransomware that encrypts the entire file on the disk even if the MS-Word file is normally executed. Therefore, it is necessary to analyze forgery and alteration of digital document through internal structure analysis of MS-Word file. In this paper, we designed and implemented a mechanism to detect this efficiently and automatic detection software, and presented a method to proactively respond to attacks such as ransomware exploiting MS-Word security vulnerabilities.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.153-162
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• 2020

Digital image forgery detection is one of very important fields in the field of digital forensics. As the forged images change naturally through the advancement of technology, it has made it difficult to detect forged images. In this paper, we use passive forgery detection for copy paste forgery in digital images. In addition, it detects copy-paste forgery using the L₀ Norm-based LE operator, and compares the detection accuracy with the forgery detection using the existing L₂, L₁ Norm-based LE operator. In comparison of detection rates, the proposed lower triangular(Ayalneh and Choi) window was more robust to BAG mismatch detection than the conventional window filter. In addition, in the case of using the lower triangular window, the performance of image forgery detection was measured increasingly higher as the L₂, L₁ and L₀ Norm LE operator was performed.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.389-395
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• 2015

Digital image provides many conveniences at the internet environment recently. A great number of applications, like Digital Library, Stock Image, Personal Image and Important Information, require the use of digital image. However it has fatal defect which is easy to be modified because digital image is only electronic file. Numerous digital image forgeries have become a serious problem due to the sophistication and accessibility of image editing software. Copy-Move forgery is the simplest type of forgery that involves copying portion of an image and paste it on different location within the image. There are many approaches to detect Copy-Move forgery, but all of them have their own limitations. In this paper, visual and invisible feature based forgery detection techniques are tested and analyzed. The analysis shows that pros and cons of these two techniques compensate each other. Therefore, a hybrid of visual based and invisible feature based forgery detection that combine the merits of both techniques is proposed. The experimental results show that the proposed algorithm has enhanced performance compared to individual techniques. Moreover, it provides more information about the forgery, like identifying copy and duplicate regions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.545-551
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• 2023

As digital information is readily available to everyone today, the adoption of digital evidence is increasing. However, it is virtually impossible to determine the authenticity of forgery in the case of a voice recording file that has gone through a sophisticated editing process along with the spread of various voice file editing tools. This study aims to prove that forgery, which is difficult to distinguish from the original file, is possible by using insertion, deletion, linking, and synthetic editing technologies in voice recording files. This study presents the difficulty of detecting forgery by encoding a forged voice file with the same extension as the original. In addition, it was shown that forgery detection is impossible if additional transition band deletion and secondary encoding are performed only for experiments in which features occurred. Through this, this study is expected to contribute to the establishment of more stringent evidence admissibility criteria for adopting voice recording files as digital evidence.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.457-466
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• 2010

In this paper, we propose a new method for detecting digital forgery that identify interpolated region between digital composited images. For detecting the interpolation factor and the tampered regions, we perform two algorithms: The first algorithm is to estimate the interpolation factors using the differential equation for forgery image along the horizontal, vertical, and diagonal directions, respectively; The second algorithm is to scan the interpolation factors along each direction for detection areas as the mask of the optical window size($64{\times}64$) in order to find out the forgery region. A detection map of the forgery is classified with the magnitude of estimated interpolation factors into colors. This detection map can be used to find out interpolated regions from the tampered image. Experimental results demonstrate the proposed algorithms are proven on several examples. We also show the proposed approach is to accurately detect interpolated regions from digital composite images.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.520-522
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• 2014

Due to the availability of easy-to-use and powerful image editing tools, the authentication of digital images cannot be taken for granted and it gives rise to non-intrusive forgery detection problem because all imaging devices do not embed watermark. Forgery detection plays an important role in this case. In this paper, an effective framework for passive-blind method for copy-move image forgery detection is proposed, based on Gabor filter which is robust to illumination, rotation invariant, robust to scale. For the detection, the suspicious image is selected and Gabor wavelet is applied from whole scale space and whole direction space. We will extract the mean and the standard deviation as the texture features and feature vectors. Finally, a distance is calculated between two textures feature vectors to determine the forgery, and the decision will be made based on that result.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.807-812
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• 2022

Recently, as the number of voice recording files submitted as court evidence increases, the number of cases claiming forgery is also increasing. If the audio recording file structure and metadata, which are objective grounds, are completely forged, it is actually impossible to detect forgery of the sophisticated audio recording file. It is extremely rare for the court to reject the file structure and metadata analysis performed with the forged audio recording file. The purpose of this study is to prove that forgery of voice recording file structure and metadata is easily possible. To this end, in this study, it was introduced that forgery detection is impossible when the 'mixed paste' function, which enables sophisticated editing based on the typification of the editing method of voice recording files, is applied. Moreover, it has been proven through experiments that forgery of file structure and metadata is possible. Therefore, a stricter standard for judging the admissibility of evidence is required when the audio recording file is adopted as digital evidence. This study will not only contribute to the standard of integrity in the adoption of digital evidence by judges, but will also contribute to the method of constructing a dataset for artificial intelligence in detecting forgery of recorded files that is expected to be developed in the future.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.149-156
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• 2023

OOXML-based MS-Office digital files are extensively utilized by businesses and organizations worldwide. However, OOXML-based MS-Office digital files are vulnerable to forgery and corruption attack by including hidden suspicious information, which can lead to activating malware or shell code being hidden in the file. Such malicious code can cause a computer system to malfunction or become infected with ransomware. To prevent such attacks, it is necessary to analyze and detect the corruption of OOXML-based MS-Office files. In this paper, we examine the weaknesses of the existing OOXML-based MS-Office file structure and analyzes how concealment and forgery are performed on MS-Office digital files. As a result, we propose a system to detect hidden data effectively and proactively respond to ransomware attacks exploiting MS-Office security vulnerabilities. Proposed system is designed to provide reliable and efficient detection of hidden data in OOXML-based MS-Office files, which can help organizations protect against potential security threats.