• 한국통신학회논문지
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• 제35권1A호
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• pp.25-32
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• 2010

최근 여객용 선박을 즐기는 다양한 층이 확대되면서 일반 여행자들에게 공개하지 못할 공간 및 정보 등이 있다. 이런 이유로 특정 선박승무원 출입만 허용할 수 있는 보안시스템이 필요하다. 현재 보안시스템에 가장 많이 사용되는 생체인식 즉 지문, 홍채, 정맥 등 다양한 방법을 사용한다. 하지만 이 방법들은 흔적을 남기므로 또 따른 목적으로 사용할 수 있는 단점을 가지고 있다. 따라서 본 논문에서는 기존 암호입력 인터페이스의 문제점인 지문이 남는 위험을 방지하고자 손동작을 2차원 입력 영상으로부터 획득하여 손동작을 인식하고 3차원 영상 큐브를 이용한 암호입력 인터페이스 설계하고 구현한다.

• 전자공학회논문지
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• 제54권2호
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• pp.76-82
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• 2017

랜섬웨어를 포함한 악성코드를 빠르게 탐지하여 빅데이터를 보호하기 위해 본 연구에서는 인공지능의 딥러닝으로 학습된 이미지 분석을 통한 악성코드 분석 기법을 제안한다. 우선 악성코드들에서 일반적으로 사용하는 2,400여개 이상의 데이터를 분석하여 인공신경망 Convolutional neural network 으로 학습하고 데이터를 이미지화 하였다. 추상화된 이미지 그래프로 변환하고 부분 그래프를 추출하여 악성코드가 나타내는 집합을 정리하였다. 제안한 논문에서 추출된 부분 집합들 간의 비교 분석을 통해 해당 악성코드들이 얼마나 유사한지를 실험으로 분석하였으며 학습을 통한 방법을 이용하여 빠르게 추출하였다. 실험결과로부터 인공지능의 딥러닝을 이용한 정확한 악성코드 탐지 가능성과 악성코드를 이미지화하여 분류함으로써 더욱 빠르고 정확한 탐지 가능성을 보였다.

• 정보보호학회논문지
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• 제18권3호
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• pp.61-68
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• 2008

본 논문에서는 지문 영상의 품질을 평가하는 새로운 방법을 제안한다. 제안한 방법은 지문 융선의 분포와 방향성, 특징점의 밀도 뿐 아니라 지문의 크기, 위치 등을 분석하여 지문 영상의 품질을 평가하게 된다. 특히 지문의 입력 위치를 분석하여 한쪽으로 치우치거나 일부만 입력된 지문을 걸러냄으로서 인식 성능을 향상시킬 수 있다. 또한 제안한 품질 평가 방법을 다양한 지문 데이터베이스에 적용하여 지문 영상의 품질과 인식 성능 간의 상관도 분석을 수행하였으며, 이를 통하여 인식 성능 향상을 위한 영상의 품질에 대한 임계값을 결정할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4588-4603
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• 2014

In this paper, we present a high-quality image authentication scheme based on absolute moment block truncation coding. In the proposed scheme, we use the parity of the bitmap (BM) to generate the authentication code for each compressed image block. Data hiding is used to authenticate whether the content has been altered or not. For image authentication, we embed the authentication code to quantization levels of each image block compressed by absolute moment block truncation coding (AMBTC) which will be altered when the host image is manipulated. The embedding position is generated by a pseudo-random number generator for security concerned. Besides, to improve the detection ability we use a hierarchical structure to ensure the accuracy of tamper localization. A watermarked image can be precisely inspected whether it has been tampered intentionally or incautiously by checking the extracted watermark. Experimental results demonstrated that the proposed scheme achieved high-quality embedded images and good detection accuracy, with stable performance and high expansibility. Performance comparisons with other block-based data hiding schemes are provided to demonstrate the superiority of the proposed scheme.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.61-70
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• 2023

Image segmentation is a very crucial step in effective digital image processing. In the past decade, several research contributions were given related to this field. However, a general segmentation algorithm suitable for various applications is still challenging. Among several image segmentation approaches, graph-based approach has gained popularity due to its basic ability which reflects global image properties. This paper proposes a methodology to partition the image with its pixel, region and texture along with its intensity. To make segmentation faster in large images, it is processed in parallel among several CPUs. A way to achieve this is to split images into tiles that are independently processed. However, regions overlapping the tile border are split or lost when the minimum size requirements of the segmentation algorithm are not met. Here the contributions are made to segment the image on the basis of its pixel using min-cut/max-flow algorithm along with edge-based segmentation of the image. To segment on the basis of the region using a homogenous optimum cut algorithm with boundary segmentation. On the basis of texture, the object type using spectral partitioning technique is identified which also minimizes the graph cut value.

• Current Optics and Photonics
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• 제8권3호
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• pp.270-281
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• 2024

Information-hiding technology is introduced into an optical ghost imaging encryption scheme, which can greatly improve the security of the encryption scheme. However, in the current mainstream research on camouflage ghost imaging encryption, information hiding techniques such as digital watermarking can only hide 1/4 resolution information of a cover image, and most secret images are simple binary images. In this paper, we propose an equal-resolution image-hiding encryption scheme based on deep learning and computational ghost imaging. With the equal-resolution image steganography network based on deep learning (ERIS-Net), we can realize the hiding and extraction of equal-resolution natural images and increase the amount of encrypted information from 25% to 100% when transmitting the same size of secret data. To the best of our knowledge, this paper combines image steganography based on deep learning with optical ghost imaging encryption method for the first time. With deep learning experiments and simulation, the feasibility, security, robustness, and high encryption capacity of this scheme are verified, and a new idea for optical ghost imaging encryption is proposed.

• 전자공학회논문지A
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• 제33A권12호
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• pp.97-103
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• 1996

This paper presents a new security verification technique based on an image encryption by a white noise image that serves as an encryption key. In the proposed method that resembles holographic process, the encryption process is executed digitally using FFT routine which gives chances for separating corruptive noise from reconstructed primary image The encoded image thus obtained is regarded as an nterference pattern caused by two lightwaves transmitted through the primary image and the white noise image. The decoding process is executed optically and in real-tiem fashion where lightwave transmitted through the white noise image illuminates the encrypted card.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.242-245
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• 2019

In this paper, watermark embedding technique was proposed to securely conceal the watermark in color cover image by applying the spatial encryption technique. The embedded watermak can be extracted from stego-image without loss. The quality of the stego-image is very good. So it is not possible to visually distinguish the difference between the original cover image and the stego-image. The validity of the proposed technique was verified by mathematical analysis. The proposed watermark embedding technique can be used for intellectual property protection, military, and medical applications that require high security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.