english
an archive of posts with this tag
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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 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 Paper Seminar Materials
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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
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
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 Paper Seminar Materials
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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 paper seminar material
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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 paper seminar material
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Prof. Hoki Kim's Research Team Publishes in Top 5% SCI Journal
Introduction to machine unlearning research in industrial AI environments
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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 paper seminar material
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CYBENCH: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models
LLM Cyber-Attack Bias Benchmark paper seminar material
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Grand Prize at the '2025 SW·AI Tech Fair' Outstanding Achievement Presentation
Lab competition and award achievement
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Prof. Hoki Kim's Paper Accepted at World's Top AI Conference
Introduction to machine unlearning and recent lab research paper
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Unlearning-Aware Minimization
Machine Unlearning and the recent research paper from our lab
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Extracting Robust Models with Uncertain Examples
Model Stealing and Application paper seminar material
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Perturbing Inputs to Prevent Model Stealing
Model Stealing and Application paper seminar material
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Preventing Neural Network Weight Stealing via Network Obfuscation
Model Stealing and Application paper seminar material
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Practical Black-Box Attacks Against Machine Learning
Model Stealing and Application paper seminar material
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High Accuracy and High Fidelity Extraction of Neural Networks
Model Stealing and Application paper seminar material
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Hiding CNN Parameters with Guided Grad-CAM
Model Stealing and Application paper seminar material
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Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Model Stealing and Application paper seminar materials
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Data-Free Model Extraction
Model Stealing and Application paper seminar materials
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PRADA: Protecting Against DNN Model Stealing Attacks
Model Stealing and Application paper seminar materials
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Towards Reverse-Engineering Black-Box Neural Networks
Model Stealing and Application paper seminar materials
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Knockoff Nets: Stealing Functionality of Black-Box Models
Model Stealing and Application paper seminar materials
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Stealing Hyperparameters in Machine Learning
Model Stealing and Application paper seminar materials
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Stealing Machine Learning Models via Prediction APIs
Model Stealing and Application paper seminar materials
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CaddieSet: A Golf Swing Dataset with Human Joint Features and Ball Information
Introduction to collaborative research from our lab
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Explaining Determinants of Bank Failure Prediction via Neural Additive Model
Lab paper introduction: AI explainability
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Evaluating Practical Adversarial Robustness of Fault Diagnosis Systems via Spectrogram-Aware Ensemble Method
Lab paper introduction: AI robustness
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Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
Machine Unlearning paper seminar material
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Towards Unbounded Machine Unlearning
Machine Unlearning paper seminar material
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Approximate Data Deletion from Machine Learning Models
Machine Unlearning paper seminar material
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SalUn: Empowering Machine Unlearning via Gradient-Based Weight Saliency in Both Image Classification and Generation
Machine Unlearning paper seminar material
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Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
Machine Unlearning paper seminar material
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Machine Unlearning of Features and Labels
Machine Unlearning paper seminar material
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Amnesiac Machine Learning
Machine Unlearning paper seminar material
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Evaluating Machine Unlearning via Epistemic Uncertainty
Machine Unlearning paper seminar material
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Are Self-Attentions Effective for Time Series Forecasting?
Lab paper introduction: AI explainability
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Key Elements and Technical Challenges of Trustworthy AI
Concepts of trustworthy AI and introduction to our lab's key technologies
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AI Regulations and Trustworthiness
International and domestic AI-related regulations and AI trustworthiness
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Stability Analysis of Sharpness-Aware Minimization
Generalization and the recent research paper from our lab
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Fantastic Robustness Measures: The Secrets of Robust Generalization
Adversarial robustness and the recent research paper from our lab