• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.569-578
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• 2021

Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.429-431
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• 2008

생체 인식 기술이 사회 전반에 걸쳐 다양하게 사용되어짐에 따라 인식기술 중의 하나인 Face Recognition 은 하루가 다르게 발전하고 있다. 하지만, 그와 함께 해킹방법도 다양화되어지고 있다. 그럼에도 불구하고, 위, 변조 영상 판별(Liveness Detection) 분야에 관련된 연구들은 초기 단계를 벗어나지 못하고 있다. 본 논문에서는 적외선 영상을 이용하여 동공부분의 반사 정도를 이용하여 실제 이미지와 위, 변조 이미지를 판별하는 방법을 제안한다.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.100-105
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• 2021

This study examines the effectiveness of the study through a case of PBL(problem-based-learning) class conducted in a balanced culture course called at 00- University in the second semester of 2020. The effects of learning are as follows: First, PBL(problem-based-learning) has sufficient active interaction between the teacher and the learner. In the face of prolonged non-face-to-face learning, the PBL teaching method has sufficient interaction between the professors-learner and the learner. Second, PBL learning can actively utilize various problems that fit the characteristics of the subject and actively utilize the process of role sharing and collaboration. By presenting various problem situations suitable for the subject, students will be able to share roles individually or as a team, and fully experience the process of collaboration and discussion in the process of investigating the data. Third, critical perceptions of problem situations can be extended. In modern times, a variety of problem situations arise and critical perceptions of them must be fully learned. In a mass production and mass consumption society, students should develop the ability to blindly recognize and distinguish between real and fake information in a flood of information. The limitations identified in this class case are, first, the nature of the subject, "Understanding Culture and Philosophy," which makes it possible to discuss the global cultural phenomenon, but it should be discussed in terms of philosophy. Second, it is not easy to work as a team on non-face-to-face online. Nevertheless, PBL is a very effective method of learning in which active interactions and learning activities take place between professors and students, whether face-to-face or face-to-face online learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1113-1121
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• 2019

In this paper, we propose the face anti-spoofing method based on combination of luminance and chrominance with convolutional neural networks. The proposed method extracts luminance and chrominance features independently from live and fake faces by using stacked convolutional neural networks and auxiliary networks. Unlike previous methods, an attention module has been adopted to adaptively combine extracted features instead of simply concatenating them. In addition, we propose a new loss function, called the contrast loss, to learn the classifier more efficiently. Specifically, the contrast loss improves the discriminative power of the features by maximizing the distance of the inter-class features while minimizing that of the intra-class features. Experimental results demonstrate that our method achieves the significant improvement for face anti-spoofing compared to existing methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.540-542
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• 2023

Due to the serious issues posed by facial manipulation technologies, many researchers are becoming increasingly interested in the identification of face forgeries. The majority of existing face forgery detection methods leverage powerful data adaptation ability of neural network to derive distinguishing traits. These deep learning-based detection methods frequently treat the detection of fake faces as a binary classification problem and employ softmax loss to track CNN network training. However, acquired traits observed by softmax loss are insufficient for discriminating. To get over these limitations, in this study, we introduce a novel discriminative feature learning based on Vision Transformer architecture. Additionally, a separation-center loss is created to simply compress intra-class variation of original faces while enhancing inter-class differences in the embedding space.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.165-166
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• 2007

특수 분장을 이용하여 매우 정교하게 제작된 가면을 쓴 얼굴 위변조의 경우 일반적인 밝기 영상으로는 검출이 용이하지 않다. 최근의 획기적인 특수 분장 기술 발전을 고려할 때 성공적인 얼굴 인식시스템 개발을 위해 가면을 쓴 얼굴 위변조 검출 연구는 매우 중요하다. 본 연구에서는 물질의 재질에 따른 반사율의 차이를 기반으로 가면을 착용하는 얼굴 위변조를 검출하는 방법을 제안한다. 우선 알비도(albedo)에 착안하여 여러 파장대의 조명에 대해 실험하였다. 실제 얼굴 인식 시스템의 적용 환경을 고려할 때 알비도를 단순히 빛의 반사량으로 간략화 할 수 있음을 보였고, 실험결과 850nm 적외선 조명이 적합하다는 결론을 얻었다.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.492-503
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• 2008

With the recent development and increasing importance of personal identification systems, biometric technologies with less risk of loss or unauthorized use are being popularized rapidly. In particular, because of their high identification rate and convenience, fingerprint identification systems are being used much more commonly than other biometric systems such as iris recognition, face recognition and vein pattern recognition. However, a fingerprint identification system has the problem that artificially forged finger-prints can be used as input data. Thus, in order to solve this problem, the present study proposed a method for detecting forged fingerprints by measuring the degree of attenuation when the light from an optical fingerprint sensor passes through the finger and analyzing changes in the transmission of light over stages at fixed intervals. In order to prove improvement in the performance of the proposed system, we conducted an experiment that compared the system with an existing multi-sensor recognition system that measures also the temperature of fingerprint. According to the results of the experiment, the proposed system improved the forged fingerprint detection rate by around 32.6% and this suggests the possibility of solving the security problem in fingerprint identification systems.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.173-178
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• 2022

Recently, various computing technologies such as artificial intelligence, big data, and IoT are developing. In particular, artificial intelligence-based deepfake technology is being used in various fields such as the content and medical industry. Deepfake technology is a combination of deep learning and fake, and is a technology that synthesizes a person's face or body through deep learning, which is a core technology of AI, to imitate accents and voices. This paper uses deepfake technology to study the creation of virtual characters through the synthesis of animation models and real person photos. Through this, it is possible to minimize various cost losses occurring in the animation production process and support writers' work. In addition, as deepfake open source spreads on the Internet, many problems emerge, and crimes that abuse deepfake technology are prevalent. Through this study, we propose a new perspective on this technology by applying the deepfake technology to children's material rather than adult material.

• Convergence Security Journal
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• v.15 no.3_1
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• pp.41-51
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• 2015

Human have more complicate and skilled ability for lying even cheat ourself. It is not easy to cheat unconscious things like sweat, eyes, or voice, but if some one cheat own self, he can cheat every of that. Lie is one of the way to spread our gene and our instinct make a lie. Every living organism even bacteria or virus use similar trick to survive. In human body, there are more complicate and profound mechanism for lying like breathe, sweat, eyes, face or voice. We can control some of that and make a fake, but it can't be perfect. Human also called 'Homo Fallax' cause we have a language and skill to lie with it. In present, we can detect lie with polygraph, but it has few weakness. So we try to use Vibraimage technology for resolve it. In this paper, we describe how to use Vibraimage for lie detection and the research history.