• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.203-211
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• 2021

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.83-93
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• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.220-228
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• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• 통상정보연구
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• 제19권1호
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• pp.67-98
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• 2017

본 연구의 목적은 미국의 국가이미지와 방문경험이 해외직접구매의도에 미치는 영향을 분석하는 것이다. 국가이미지의 구성요소로써 경제기술이미지, 사회문화이미지 그리고 국민이미지를 사용하였으며, 총 155명의 국내소비자를 대상으로 설문조사를 실시하였다. 데이터분석에는 단순회귀분석, 다중회귀분석 그리고 t검증이 사용되었다. 국가이미지는 해외직구의도를 예측함에 있어 매우 중요한 요인으로 결과가 도출되었으며 다중회귀분석 결과는 국가이미지의 구성요소 중 사회문화이미지가 해외직구의도에 가장 중요한 영향을 미치며, 경제기술이미지 또한 통계적으로 유의한 영향을 미치는 것으로 나타났다. 그러나, 국민이미지는 국내 소비자가 해외직구를 할 때 고려하지 않는 것으로 분석되었다. 미국 방문경험 여부에 따른 해외직구의도에 차이가 있는지 살펴본 결과 이전에 미국을 방문을 경험해 본 소비자는 방문경험이 없는 소비자 보다 해외직구를 할 의향이 높은 것으로 나타났다. 본 연구의 결과는 해외직구 소비자 이해의 폭을 넓히고 해외직판을 계획하고 있는 기업들의 향후 판매전략과 마케팅전략을 수립함에 있어서 유용한 정보를 제공하는데 있다.

• 복합신소재구조학회 논문집
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• 제6권3호
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• pp.20-25
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• 2015

This paper introduces a digital image processing(DIP) method as a method for measuring the displacement of pylon. The comparison of DIP results and ANSYS analysis results verified the validity of the image processing technique. Normalized cross-correlation(NCC) coefficient was used and experiments were performed three times. It shows that the displacement difference was 22% and 5% compared to ANSYS results. Therefore, the image processing method is expected to be able to measure the displacement of pylon sufficiently.

• 통상정보연구
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• 제9권1호
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• pp.23-41
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• 2007

This paper research the country image, the brand image, consumers' performances and the brand royalty with the path analysis on foreign automobile brands. Our empirical study testify the reliability analysis, the factor analysis and the path analysis with above variables by SPSS 12.0 and AMOS 4.0. The result of this research is that hypotheses H1, H3, H5 were significantly supported, whereas, hypotheses H2, H4 were rejected in the samples of American automobile. and in the samples of Japanese automobile, it was clearly revealed that hypotheses H2, H3, H4 were significantly supported, whereas, hypotheses H1, H5 were rejected. So, in Korean market, we should focus on the brand image strategy more than the country image to make the brand royalty with American automobile products. whereas, Japanese automobile products, another strategy is recommended.

• International Journal of Advanced Culture Technology
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• 제4권2호
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• pp.1-6
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• 2016

Impervious surfaces are important indicators for urban development monitoring. Accurate mapping of urban impervious surfaces with observational satellites, such as VNREDSat-1, remains challenging due to the spectral diversity not captured by an individual PAN image. In this article, five multi-resolution image fusion techniques were compared for the task of classifting urban impervious surfaces. The result shows that for VNREDSat-1 dataset, UNB and Wavelet tranformation methods are the best techniques in reserving spatial and spectral information of original MS image, respectively. However, the UNB technique gives the best results when it comes to impervious surface classification, especially in the case of shadow areas included in non-impervious surface group.

• International Journal of Advanced Culture Technology
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• 제9권1호
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• pp.105-113
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• 2021

Through the analysis of Gerhard Richter's works of art in the period of "fuzzy image", this paper expounds the special internal quality of fuzzy image in Richter's works and reveals an important direction of the development of contemporary easel painting, The special essence of Richter's vague image is that he uses photos to reflect the power and authenticity of the existence of the objective things. He was not satisfied with the radical way of modern painting and tried to return to the traditional way of painting, but in fact Richter kept a special distance between classical painting and modern art. Richter not only blurs the image in his creation, but also shows that he wants to show the objectivity of things, keep the distance, authenticity and give up painting. Richter used fuzzy images to capture the concept of "visual unconsciousness", and finally separated from the concept. When we read how Richter showed the reality of things in the way of painting, we also saw a kind of contradictory psychology when he faced the complicated objective.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.73-82
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• 2022

This paper shows the hiding process of unlimited secret text size in an image using three methods: the first method is the traditional method in steganography that based on the concealing the binary value of the text using the least significant bits method, the second method is a new method to hide the data in an image based on Exclusive OR process and the third one is a new method for hiding the binary data of the text into an image (that may be grayscale or RGB images) using Exclusive and Huffman Coding. The new methods shows the hiding process of unlimited text size (data) in an image. Peak Signal to Noise Ratio (PSNR) is applied in the research to simulate the results.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.