• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.97-98
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• 2024

4차 산업 혁명의 시작으로 인공지능이 빠르게 발달함에 따라 현재 생성형 인공지능이 주목받고 있다. 이에 따라 딥보이스 기술과 딥페이크 기술을 활용하여 다양한 범죄가 발생하고 있어 관련 사례와 이를 해결하기 위해 진행 중인 연구에 대해서 조사하였다. 딥보이스와 딥페이크를 탐지하는 연구는 지속되고 있지만 관련 기술이 상용화되어 있지 않아 범죄를 예방하기에는 부족한 실정이다. 범죄에 악용되는 속도가 빨라지고 있는 만큼 더 많은 연구가 신속하게 이루어져야 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.811-814
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• 2021

본 논문에서는 점차 진화되어가는 보이스피싱 수법에 대하여 딥러닝 기반 네트워크인 DNN(Deep Neural Network)를 통한 보이스피싱 여부 판별할 뿐만 아니라, CNN, Bi-LSTM을 활용한 다양한 관점에서의 감성 분석을 통하여 보이스피싱 조직원의 감성 상태를 파악하여 판별된 결과에 신뢰도를 높여주는 모델을 제안하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.95-96
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• 2024

본 논문은 금융 범죄 중 하나인 보이스피싱을 실시간으로 예방하기 위한 탐지 기법을 제안한다. 제안된 모델은 수화기에 출력되는 음성을 녹음하고 네이버 CSR(Cloud Speech Recognition)을 통해 텍스트 파일로 변환한 후 딥러닝 기반의 KoBERT를 바탕으로 다양한 보이스피싱 패턴을 학습하여 실시간 환경에서의 신속하고 정확한 탐지를 위해 실제 통화 데이터를 적절하게 처리하여, 이를 통해 효과적인 보이스피싱 예방에 도움을 줄 것으로 예상된다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.437-446
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• 2022

Text classification task from Natural Language Processing (NLP) combined with state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms as the core engine is widely used to detect and classify voice phishing call transcripts. While numerous studies on the classification of voice phishing call transcripts are being conducted and demonstrated good performances, with the increase of non-face-to-face financial transactions, there is still the need for improvement using the latest NLP technologies. This paper conducts a benchmarking of Korean voice phishing detection performances of the pre-trained Korean language model KoBERT, against multiple other SOTA algorithms based on the classification of related transcripts from the labeled Korean voice phishing dataset called KorCCVi. The results of the experiments reveal that the classification accuracy on a test set of the KoBERT model outperforms the performances of all other models with an accuracy score of 99.60%.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.521-523
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• 2018

본 논문은 보이스피싱 발생 후의 대처 방안이 아닌, 범죄 행위 자체의 예방을 목적으로 하는 신원 허가 후의 자동인출기 시스템을 제안한다. 범죄예방 현금인출기(ATM)의 작동과정은 크게 두 가지이다. 첫째, YOLO Detection System을 이용하여 학습된 데이터에 기반하여, 사용자의 얼굴에서 선글라스와 마스크를 검출한다. 둘째, 미리 학습된 범죄자 모델 데이터에 앞서 사용자의 신원을 조회하고 ATM의 사용허가를 내준다. 혹은 주요지명 피의자일 경우, 경찰에 실시간 안내를 주어 범죄 수사를 용이하게 한다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.23-30
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• 2024

In this paper, we propose a deep learning-based personalized senior care service application. The proposed application uses Speech to Text technology to convert the user's speech into text and uses it as input to Autogen, an interactive multi-agent large-scale language model developed by Microsoft, for user convenience. Autogen uses data from previous conversations between the senior and ChatBot to understand the other user's intent and respond to the response, and then uses a back-end agent to create a wish list, a shared calendar, and a greeting message with the other user's voice through a deep learning model for voice cloning. Additionally, the application can perform home IoT services with SKT's AI speaker (NUGU). The proposed application is expected to contribute to future AI-based senior care technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.439-449
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• 2024

Recent rapid advancements in voice generation technology have enabled the natural synthesis of voices using text alone. However, this progress has led to an increase in malicious activities, such as voice phishing (voishing), where generated voices are exploited for criminal purposes. Numerous models have been developed to detect the presence of synthesized voices, typically by extracting features from the voice and using these features to determine the likelihood of voice generation.This paper proposes a new model for extracting voice features to address misuse cases arising from generated voices. It utilizes a deep learning-based audio codec model and the pre-trained natural language processing model BERT to extract novel voice features. To assess the suitability of the proposed voice feature extraction model for voice detection, four generated voice detection models were created using the extracted features, and performance evaluations were conducted. For performance comparison, three voice detection models based on Deepfeature proposed in previous studies were evaluated against other models in terms of accuracy and EER. The model proposed in this paper achieved an accuracy of 88.08%and a low EER of 11.79%, outperforming the existing models. These results confirm that the voice feature extraction method introduced in this paper can be an effective tool for distinguishing between generated and real voices.