• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.465-466
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• 2023

AI 기술의 발전으로 AI가 그린 그림과 인간이 직접 그린 그림을 식별하는 것이 어려워졌다. AI 기술을 통해 작품을 특정 화풍으로 그리는 것이 쉬워져 작품 도용과 평가 절하가 증가하고 있으며, AI가 인간과 유사하게 그림을 표현하는 경우 딥페이크 피싱과 같은 악용 사례도 늘어나고 있다. 따라서 본 논문에서는 AI 그림을 식별하기 위한 인공지능 모델 개발을 목표로 하고 있으며, 데이터셋을 구축하여 인공지능 기술을 활용한 알고리즘을 개발한다. YOLO Segmentation과 CNN을 활용하여 학습을 진행하고, 이를 통해 도용과 딥페이크 피해를 방지하는 프로세스를 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.269-270
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• 2024

디지털 오디오 파일의 보안은 디지털 미디어의 확산과 함께 점차 중요해지고 있다. 특히, 딥페이크와 같은 기술을 이용한 조작이 증가함에 따라, 이를 효과적으로 방지하는 기술이 대두되고 있다. 본 연구에서는 연성 워터마킹 기술을 활용하여, 오디오 파일이 외부 조작에 의해 변경되었을 때 오디오 파일이 의도적으로 파괴하는 방식을 제안한다. 본 논문에서는 연성 워터마크 생성 및 삽입 방법에 관한 자세한 설명을 하고, 연성 워터마킹을 통해 오디오의 변조 여부를 즉각적으로 탐지하는데 어떻게 기여하는지를 보여준다. 제안 기법은 오디오 원본의 무결성을 효과적으로 보호하는 새로운 방법을 제시하며, 디지털 미디어 보안을 강화하는데 중요한 역할을 할 것으로 기대된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1053-1065
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• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.91-94
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• 2021

As deep learning technologies becoming developed, realistic fake videos synthesized by deep learning models called "Deepfake" videos became even more difficult to distinguish from original videos. As fake news or Deepfake blackmailing are causing confusion and serious problems, this paper suggests a novel model detecting Deepfake videos. We chose Residual Convolutional Neural Network (Resnet50) as an extraction model and Long Short-Term Memory (LSTM) which is a form of Recurrent Neural Network (RNN) as a classification model. We adopted cosine similarity with hinge loss to train our extraction model in embedding the features of Deepfake and original video. The result in this paper demonstrates that temporal features in the videos are essential for detecting Deepfake videos.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1224-1241
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• 2021

Recent development of deep learning techniques for image generation has led to straightforward generation of sophisticated deepfakes. However, as a result, privacy violations through deepfakes has also became increased. To solve this issue, a number of techniques for deepfake detection have been proposed, which are mainly focused on RGB channel-based analysis. Although existing studies have suggested the effectiveness of other color model-based analysis (i.e., Grayscale), their effectiveness has not been quantitatively validated yet. Thus, in this paper, we compare the effectiveness of Grayscale channel-based analysis with RGB channel-based analysis in deepfake detection. Based on the selected CNN-based models and deepfake datasets, we measured the performance of each color model-based analysis in terms of accuracy and time. The evaluation results confirmed that Grayscale channel-based analysis performs better than RGB-channel analysis in several cases.

• Convergence Security Journal
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• v.24 no.2
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• pp.63-70
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• 2024

With the development of artificial intelligence technology, our lives are changing in innovative ways, but at the same time, new ethical issues are emerging. In particular, issues of discrimination due to algorithm and data bias, deep fakes, and personal information leakage issues are judged to be social priorities that must be resolved as artificial intelligence services expand. To this end, this paper examines the concept of artificial intelligence and ethical issues from the perspective of artificial intelligence ethics, and includes each country's ethical guidelines, laws, artificial intelligence impact assessment system, artificial intelligence certification system, and the current status of technologies related to artificial intelligence algorithm transparency to prevent this. We would like to examine and suggest institutional improvement measures to strengthen artificial intelligence ethics.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.229-239
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• 2021

For this study, we have extensively analyzed the presentation data of the government's financial-related departments and the data of each financial institution and electronic financial institution.. As a result, In Korea's electronic financial environment, real changes such as first) expansion of non-face-to-face finance, second) teleworking in the financial sector, third) abolition of accredited certification, fourth) advanced voice phishing, fifth) openness of the financial industry and diversification of forms, sixth) the'walletless society'. In addition to the above, however, global changes triggered by the Fourth Industrial Revolution spread to the financial security sector, making it difficult to respond to problems such as artificial intelligence/ deep learning/ user analysis/ deepfake technology. As the proportion of electronic finance is increasing socially, it should be studied in the fields of electronic finance and its environment, and crime and criminal investigation.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.1-9
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• 2021

Recently, new types of security crimes have been steadily occurring in Korea due to environmental changes such as the development of IT technology, and responding to these threats has become a key national task for the establishment of a safe society as well as individuals and businesses. Meanwhile, as the Zero-Contact Era has arrived since the COVID-19 Pandemics, a new convergence security threat that combines the characteristics of the Zero-Contact Era with the existing IT development is threatening our society. Research to prevent and correct these new levels of threats is continuously required in this study. Chapter 1 examined the causes of new convergence security threats and Chapter 2 discussed cybersecurity, fake news, remote voting·work and video security threats as five major threats.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.193-207
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• 2021

As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.