• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.14-32
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• 2022

This study aims to examine the perception of false information and deepfakes according to the experience of being exposed to false information and deepfake images for YouTube content users. The study used the data from 'YouTube Use and False Information Exposure Experience' conducted by the Korea Press Foundation in 2018. For the statistical analysis, correspondent analysis was employed. The main results followed as: First, it was found that men who have been exposed to false information are most seriously aware of the problems caused by false information on YouTube. Second, regarding the need for regulation on deepfake images, women who have experienced exposure to deepfake images tended to agree, and women had a stronger awareness of the need for regulation due to damage to deepfake images than men. While YouTube users generally agree that regulation is necessary, it is required to educate YouTube users about the types of disinformation and deepfakes. In particular, it is considered to be desirable to create an environment for the self-regulation of the producers and distributors.

• 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.

• Review of KIISC
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• v.30 no.5
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• pp.79-92
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• 2020

최근 전 세계적으로 '가짜뉴스', '가짜 연예인 음란 동영상' 및 '지인 능욕'에 사용되는 인공지능 기반의 딥페이크(Deepfakes)기술이 사회적인 이슈로 대두되고 있다. 딥페이크 기술이란 딥러닝 기술을 이용해 악의적으로 조작된 음성, 영상, 이미지 등을 만들어 내는 방법으로, 인공지능 기술의 발전에 맞추어 더욱더 빠르고 정교한 생성 기술이 등장하고 있다. 이러한 딥페이크 기술은 빠른 개발 속도와 쉬운 접근성을 기반으로 다양한 범죄에 악용되고 있다. 본 논문에서는 다양한 딥페이크 생성 기술을 설명하고, 이를 효율적으로 탐지 할 수 있는 다양한 데이터 기반 딥페이크 탐지 기술의 현황을 설명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.560-563
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• 2022

인공지능 기반의 딥페이크(Deepfakes) 기술이 사회적인 이슈로 대두되고 있다. 하지만 기존 딥페이크 탐지기는 sharpening, additive noise와 같은 간단한 이미지 변형만으로 탐지 우회가 가능한 문제점이 있다. 본 논문에서는 안티 포렌식에 강인한 딥페이크 탐지기를 개발하기 위해 이미지 편집 도구 기반의 안티 포렌식 데이터셋을 생성하고 적대적 학습을 수행하는 방법을 제안한다. 실험 결과를 통해 안티 포렌식에 취약한 기존 딥페이크 탐지기 성능이 제안한 적대적 학습 기법을 수행한 이후에 탐지율이 크게 개선된 것을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2230-2252
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• 2024

Advances in deep learning technology have enabled the generation of more realistic deepfakes, which not only endanger individuals' identities but also exploit vulnerabilities in face recognition systems. The majority of existing deepfake detection methods have primarily focused on RGB-based analysis, offering unreliable performance in terms of detection accuracy and time. To address the issue, a grayscale-based deepfake detection method has recently been proposed. This method significantly reduces detection time while providing comparable accuracy to RGB-based methods. However, despite its significant effectiveness, the "key components" that directly affect the performance of grayscale-based deepfake detection have not been systematically analyzed. In this paper, we target three key components: RGB-to-grayscale conversion method, brightness level in grayscale, and resolution level in grayscale. To analyze their impacts on the performance of grayscale-based deepfake detection, we conducted comprehensive evaluations, including component-wise analysis and comparative analysis using real-world datasets. For each key component, we quantitatively analyzed its characteristics' performance and identified differences between them. Moreover, we successfully verified the effectiveness of an optimal combination of the key components by comparing it with existing deepfake detection methods.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1044-1058
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• 2021

Tempered electronic contents have multiplied in last few years, thanks to the emergence of sophisticated artificial intelligence(AI) algorithms. Deepfakes (fake footage, photos, speech, and videos) can be a frightening and destructive phenomenon that has the capacity to distort the facts and hamper reputation by presenting a fake reality. Evidence of ownership or authentication of digital material is crucial for combating the fabricated content influx we are facing today. Current solutions lack the capacity to track digital media's history and provenance. Due to the rise of misrepresentation created by technologies like deepfake, detection algorithms are required to verify the integrity of digital content. Many real-world scenarios have been claimed to benefit from blockchain's authentication capabilities. Despite the scattered efforts surrounding such remedies, relatively little research has been undertaken to discover where blockchain technology can be used to tackle the deepfake problem. Latest blockchain based innovations such as Smart Contract, Hyperledger fabric can play a vital role against the manipulation of digital content. The goal of this paper is to summarize and discuss the ongoing researches related to blockchain's capabilities to protect digital content authentication. We have also suggested a blockchain (smart contract) dependent framework that can keep the data integrity of original content and thus prevent deepfake. This study also aims at discussing how blockchain technology can be used more effectively in deepfake prevention as well as highlight the current state of deepfake video detection research, including the generating process, various detection algorithms, and existing benchmarks.

• 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.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.13-19
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• 2023

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.