2026
an archive of posts from this year
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윤정현 학부연구원, 대만 이지스 해킹대회(AEGIS CTF) 국제 초청전 우승
윤정현 학부연구원이 팀 비오비(TEAM BoB)로 출전한 대만 이지스 해킹대회(AEGIS CTF) 국제 초청전에서 1위를 차지
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Research Intern Junghyun Yoon Wins the AEGIS CTF International Invitational in Taiwan
Research intern Junghyun Yoon, competing with TEAM BoB, took first place at the AEGIS CTF International Invitational in Taiwan
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중앙대학교 산업보안학과 학부생 논문, 인공지능 분야 저명 국제학술대회 채택
AI 보안 에이전트의 공격 선택 편향과 최근 연구실 논문 소개
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Undergraduate Researchers' Paper from the Department of Industrial Security Accepted at a Leading AI Conference
Attack-selection bias of AI security agents and the recent research paper from our lab
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중앙대학교 산업보안학과 학부생 논문, SCIE 상위 5% 저널 Advanced Engineering Informatics 게재
베어링 고장 진단을 위한 머신 언러닝과 최근 연구실 논문 소개
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Undergraduate Researchers' Paper from the Department of Industrial Security Published in Advanced Engineering Informatics
Machine unlearning for bearing fault diagnosis and the recent research paper from our lab
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중앙대학교 산업보안학과 김호기 교수 연구진, 과기정통부 「사이버 보안 특화 AI 파운데이션 모델 개발」 사업 선정
과기정통부 「사이버 보안 특화 AI 파운데이션 모델 개발」 사업에 네이버클라우드 컨소시엄 참여기관으로 선정
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Prof. Hoki Kim's Research Team Selected for MSIT Cybersecurity-Specialized AI Foundation Model Project
Selected as a consortium participant in the MSIT Cybersecurity-Specialized AI Foundation Model Development project led by NAVER Cloud
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MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models
멀티모달(Multi-modal) 논문 세미나 자료
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MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models
Multimodal Paper Seminar Materials
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Jailbreak in Pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
멀티모달(Multi-modal) 논문 세미나 자료
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Jailbreak in Pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
Multimodal Paper Seminar Materials
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UMU-Bench: Closing the Modality Gap in Multimodal Unlearning Evaluation
멀티모달(Multi-modal) 논문 세미나 자료
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UMU-Bench: Closing the Modality Gap in Multimodal Unlearning Evaluation
Multimodal Paper Seminar Materials
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Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
멀티모달(Multi-modal) 논문 세미나 자료
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Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
Multimodal Paper Seminar Materials
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Visual Adversarial Examples Jailbreak Aligned Large Language Models
멀티모달(Multi-modal) 논문 세미나 자료
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Visual Adversarial Examples Jailbreak Aligned Large Language Models
Multimodal Paper Seminar Materials
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On the Adversarial Robustness of Multi-Modal Foundation Models
멀티모달(Multi-modal) 논문 세미나 자료
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On the Adversarial Robustness of Multi-Modal Foundation Models
Multimodal Paper Seminar Materials
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Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
멀티모달(Multi-modal) 논문 세미나 자료
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Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
Multimodal Paper Seminar Materials
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Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
멀티모달(Multi-modal) 논문 세미나 자료
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Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
Multimodal Paper Seminar Materials
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Qwen Series
멀티모달(Multi-modal) 논문 세미나 자료
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Qwen Series
Multimodal Paper Seminar Materials
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MiniGPT-4
멀티모달(Multi-modal) 논문 세미나 자료
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MiniGPT-4
Multimodal Paper Seminar Materials
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LLaVA Series: LLaVA-1, LLaVA-1.5
멀티모달(Multi-modal) 논문 세미나 자료
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LLaVA Series: LLaVA-1, LLaVA-1.5
Multimodal Paper Seminar Materials
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Flamingo Series: Flamingo, OpenFlamingo
멀티모달 세미나 자료
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Flamingo Series: Flamingo, OpenFlamingo
Multimodal Paper Seminar Materials
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BLIP Series: BLIP, BLIP-2, InstructBLIP
멀티모달(Multi-modal) 논문 세미나 자료
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BLIP Series: BLIP, BLIP-2, InstructBLIP
Multimodal Paper Seminar Materials
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Vision-Language Model의 진화 (BLIP부터 Qwen까지)
멀티모달 비전-언어 모델(BLIP·Flamingo·LLaVA·MiniGPT-4·Qwen) 세미나 시리즈 총정리
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KISA·SK쉴더스와 공동 제작한 「AI 보안 위협 대응 매뉴얼」 배포
AI 보안 위협 대응 매뉴얼 배포 (KISA·SK쉴더스 공동)
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AI Security Threat Mitigation Manual Released with KISA and SK Shieldus
AI Security Threat Mitigation Manual released (with KISA and SK Shieldus)
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중앙대학교 산업보안학과 김호기 교수 연구진, ICML 2026 논문 3편 채택
연구실 논문 3편, ICML 2026 동시 채택
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Prof. Hoki Kim's Research Team Has Three Papers Accepted to ICML 2026
Three lab papers accepted to ICML 2026
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Stability Analysis of Sharpness-Aware Minimization
연구실 논문 소개: 인공지능 일반화
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Stability Analysis of Sharpness-Aware Minimization
Generalization and the recent research paper from our lab
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Proactive Defense Benchmark Against Deepfake Generation
연구실 논문 소개: 딥페이크 사전적 방어
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Proactive Defense Benchmark Against Deepfake Generation
Proactive deepfake defense and the recent research paper from our lab
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Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models
연구실 논문 소개: 오픈웨이트 모델 거버넌스
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Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models
Open-weight model governance and the recent research paper from our lab
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Adversarial Training for Free!
Adversarial Robustness 논문 세미나 자료
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Adversarial Training for Free!
Adversarial Robustness Paper Seminar Materials
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Adversarial Examples Are Not Bugs, They Are Features
Adversarial Robustness 논문 세미나 자료
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Adversarial Examples Are Not Bugs, They Are Features
Adversarial Robustness Paper Seminar Materials reinterpreting adversarial examples as non-robust features learned from data.
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Theoretically Principled Trade-off between Robustness and Accuracy
Adversarial Robustness 논문 세미나 자료
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Theoretically Principled Trade-off between Robustness and Accuracy
Adversarial Robustness Paper Seminar Materials
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Robustness May Be at Odds with Accuracy
Adversarial Robustness 논문 세미나 자료
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Robustness May Be at Odds with Accuracy
Adversarial Robustness Paper Seminar Materials
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Code Review: Adversarial Attacks and Defenses
torchattacks · MAIR 라이브러리 기반 적대적 공격·방어 기법 코드 리뷰
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Code Review: Adversarial Attacks and Defenses
Line-by-line PyTorch walkthrough of torchattacks and MAIR implementations of adversarial attacks and defenses.
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Towards Evaluating the Robustness of Neural Networks
Adversarial Robustness 논문 세미나 자료
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Towards Evaluating the Robustness of Neural Networks
C&W attacks expose that defensive distillation only masked existing attack weaknesses, redefining how adversarial robustness is evaluated.
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Obfuscated Gradients Give a False Sense of Security
Adversarial Robustness 논문 세미나 자료
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Obfuscated Gradients Give a False Sense of Security
Adversarial Robustness Paper Seminar Materials
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Adversarial Examples in the Physical World
Adversarial Robustness 논문 세미나 자료
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Adversarial Examples in the Physical World
Adversarial Robustness Paper Seminar Material
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Towards Deep Learning Models Resistant to Adversarial Attacks
Adversarial Robustness 논문 세미나 자료
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Towards Deep Learning Models Resistant to Adversarial Attacks
Adversarial Robustness paper seminar material
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Intriguing Properties of Neural Networks
Adversarial Robustness 논문 세미나 자료
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Intriguing Properties of Neural Networks
Adversarial Robustness Paper Seminar Material
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Explaining and Harnessing Adversarial Examples
Adversarial Robustness 논문 세미나 자료
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Explaining and Harnessing Adversarial Examples
Adversarial Robustness paper seminar material
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중앙대학교 산업보안학과 김호기 교수 연구진, 상위 10% SCI 저널 게재
산업 AI 환경에서의 머신 언러닝 연구 성과 소개
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Prof. Hoki Kim's Research Team Publishes in Top 10% SCI Journal
Introduction to machine unlearning research in industrial AI environments
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AI의 내재적 위협과 외재적 위협
AI의 내재적 위협과 외재적 위협, 그리고 신뢰성 확보 핵심 요소
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Intrinsic and Extrinsic Threats of AI
The two layers of AI threats and the essentials of trustworthy AI
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Adversarial Retain-Free Unlearning for Bearing Prognostics and Health Management
머신 언러닝과 최근 연구실 논문 소개
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Adversarial Retain-Free Unlearning for Bearing Prognostics and Health Management
Machine Unlearning and the recent research paper from our lab
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BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems
LLM Cyber-Attack Bias Benchmark 논문 세미나 자료
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BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems
LLM Cyber-Attack Bias Benchmark paper seminar material
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CYBENCH: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models
거대 언어 모델 사이버 공격 편향 벤치마크(LLM Cyber-Attack Bias Benchmark) 논문 세미나 자료
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CYBENCH: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models
LLM Cyber-Attack Bias Benchmark paper seminar material