• 한국멀티미디어학회논문지
• /
• 제22권5호
• /
• pp.535-546
• /
• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• 전자통신동향분석
• /
• 제34권4호
• /
• pp.15-22
• /
• 2019

Recent technological advances in three-dimensional (3D) sensing devices and machine learning such as deep leaning has enabled data-driven 3D applications. Research on artificial intelligence has developed for the past few years and 3D deep learning has been introduced. This is the result of the availability of high-quality big data, increases in computing power, and development of new algorithms; before the introduction of 3D deep leaning, the main targets for deep learning were one-dimensional (1D) audio files and two-dimensional (2D) images. The research field of deep leaning has extended from discriminative models such as classification/segmentation/reconstruction models to generative models such as those including style transfer and generation of non-existing data. Unlike 2D learning, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become increasingly popular owing to advances in 3D vision technology, the generation/acquisition of 3D data is still very difficult. Even if 3D data can be acquired, post-processing remains a significant problem. Moreover, it is not easy to directly apply existing network models such as convolution networks owing to the various ways in which 3D data is represented. In this paper, we summarize technological trends in AI-based 3D content generation.

• 정보보호학회논문지
• /
• 제34권3호
• /
• pp.451-461
• /
• 2024

최근 거대 언어 모델을 기반으로 한 다양한 인공지능 챗봇이 출시되고 있다. 챗봇은 대화형 프롬프트를 통해 사용자에게 빠르고 간편하게 정보를 제공할 수 있다는 이점을 가지고 있어서 질의응답, 글쓰기, 프로그래밍 등 다양한 분야에서 활용되고 있다. 그러나 최근에는 챗봇의 취약점을 악용하는 '프롬프트 주입 공격'이 제안되었는데, 이는 챗봇이 기입력된 지시사항을 위반하도록 하는 공격이다. 이와 같은 공격은 거대 언어 모델 내부의 기밀 정보를 유출하거나 또 다른 악성 행위를 유발할 수 있어서 치명적이다. 반면 이들에 대한 취약점 여부가 한국어 프롬프트를 대상으로는 충분히 검증되지 않았다. 따라서 본 논문에서는 널리 사용되는 챗봇인 ChatGPT를 대상으로 악성 한국어 프롬프트를 생성하여 공격을 수행해보고, 이들에 대한 실행 가능성을 분석하고자 한다. 이를 위해 기존에 제안된 프롬프트 주입 공격 기법을 분석하여 악의적인 한국어 프롬프트를 자동으로 생성하는 시스템을 제안하고자 한다. 특히 유해 표현을 유도하는 악성 프롬프트를 중점적으로 생성하였고 이들이 실제 유효함을 보이도록 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권6호
• /
• pp.2627-2647
• /
• 2016

With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.

• 전기전자학회논문지
• /
• 제2권1호
• /
• pp.24-33
• /
• 1998

Independent Component analysis (ICA) is a new statistical method for extracting statistically independent components from their linear instantaneous mixtures which are generated by an unknown linear generative model. The recognition model is learned in unsupervised manner so that the recovered signals by the recognition model become the possibly scaled estimates of original source signals. This paper addresses the neural learning approach to ICA. As recognition models a linear feedforward network and a linear feedback network are considered. Associated learning algorithms for both networks are derived from maximum likelihood and information-theoretic approaches, using natural Riemannian gradient [1]. Theoretical results are confirmed by extensive computer simulations.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
• /
• pp.104-107
• /
• 1996

This paper focuses on a automation selection of optimal cutting conditions and cycle time for multi-spindle metal cutting machines based on machining parameters and tool change schemes which are the two most common terms used in the metal cutting. In this research we used two step generative approach, step 1 is mathematical modeling for the selection fo optimal cutting conditions and the other is GMDH-Type modeling to estimate the system performance evaluation. We developed computer programs for these models and the fitting manufacturing examples are applied to this model and it was shown that the proposed approach has a good potential and offers a valuable tools to analyse the metal cutting system.

• 한국생산제조학회지
• /
• 제19권2호
• /
• pp.251-259
• /
• 2010

It is presented in this study a new efficient intelligent machining strategy, which can be used to remove the uncut volume at the boundary region of curved surfaces caused by cutter interference. The geometric form definitions and recognition of topological features of the surface triangulation mesh are used to generate cutter paths along successive and interconnected steepest pathways, that minimize the cusp height left after flat end milling. In order to machine the uncut volume gradually, the z-map cutter centers are adjusted to avoid cutter interference for the 6 kinds of avoidance types. And then, the generative subsequent paths are sequenced to determine the second step cutter paths for the next uncut volume. For the 2 kinds of test models with convex and concave surface region, the implemented software algorithm is evaluated by investigating the residual swelling of uncut volume for each machining step.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제14권3호
• /
• pp.162-170
• /
• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2019년도 추계학술발표대회
• /
• pp.942-945
• /
• 2019

현재 딥러닝은 컴퓨터 분야에서 이미지 처리 방법으로 활용도가 높아지면서 딥러닝 모델 개발 연구가 활발히 진행되고 있다. 딥러닝 모델 중에서 이미지 생성모델은 대표적으로 GAN(Generative Adversarial Network, 생성적 적대 신경망) 모델을 활용하고 있다. GAN은 생성기 네트워크와 판별기 네트워크를 이용하여 진짜 같은 이미지를 생성한다. 생성된 이미지는 실제 이미지와의 오차를 최소화해야 하며 이때 사용하는 함수를 손실함수라고 한다. GAN에서 손실함수는 이미지를 생성하는 학습이 불안정하여 이미지 품질이 떨어지는 문제가 있다. 개선된 GAN 관련 연구가 진행되고 있지만 완전한 문제 해결에는 부족하다. 본 논문은 7개의 GAN 모델에서 사용하는 손실함수를 분류하고 특징을 분석한다.

• 대한두개안면성형외과학회지
• /
• 제22권5호
• /
• pp.223-231
• /
• 2021

The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.