• Title/Summary/Keyword: Generative AI Crimes

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Taxonomy and Countermeasures for Generative Artificial Intelligence Crime Threats (생성형 인공지능 관련 범죄 위협 분류 및 대응 방안)

  • Woobeen Park;Minsoo Kim;Yunji Park;Hyejin Ryu;Doowon Jeong
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.34 no.2
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    • pp.301-321
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    • 2024
  • Generative artificial intelligence is currently developing rapidly and expanding industrially. The development of generative AI is expected to improve productivity in most industries. However, there is a probability for exploitation of generative AI, and cases that actually lead to crime are emerging. Compared to the fast-growing AI, there is no legislation to regulate the generative AI. In the case of Korea, the crimes and risks related to generative AI has not been clearly classified for legislation. In addition, research on the responsibility for illegal data learned by generative AI or the illegality of the generated data is insufficient in existing research. Therefore, this study attempted to classify crimes related to generative AI for domestic legislation into generative AI for target crimes, generative AI for tool crimes, and other crimes based on ECRM. Furthermore, it suggests technical countermeasures against crime and risk and measures to improve the legal system. This study is significant in that it provides realistic methods by presenting technical countermeasures based on the development stage of AI.

A Study on Fuzzy Searching Algorithm and Conditional-GAN for Crime Prediction System (범죄예측시스템에 대한 퍼지 탐색 알고리즘과 GAN 상태에 관한 연구)

  • Afonso, Carmelita;Yun, Han-Kyung
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.14 no.2
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    • pp.149-160
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    • 2021
  • In this study, artificial intelligence-based algorithms were proposed, which included a fuzzy search for matching suspects between current and historical crimes in order to obtain related cases in criminal history, as well as conditional generative adversarial networks for crime prediction system (CPS) using Timor-Leste as a case study. By comparing the data from the criminal records, the built algorithms transform witness descriptions in the form of sketches into realistic face images. The proposed algorithms and CPS's findings confirmed that they are useful for rapidly reducing both the time and successful duties of police officers in dealing with crimes. Since it is difficult to maintain social safety nets with inadequate human resources and budgets, the proposed implemented system would significantly assist in improving the criminal investigation process in Timor-Leste.