Information Laboratory (InfoLab) @ SKKU

Defending AI systems against adversarial attacks, malware, binary analysis, and behavioral authentication. Applying deep learning to medical imaging, disease detection, and multimodal clinical decision support. The growing integration of deep learning (DL) models into high-stakes domains, such as healthcare, finance, and autonomous syst... Adversarial attacks pose a serious challenge to the reliability and security of deep learning (DL) models. These attacks, often... The dynamic evolution of malware, combined with increasingly sophisticated evasion techniques such as packing, obfuscation, and... The project seeks to address the growing need for transparency, accountability, and interpretability in artificial intelligence... Traditional authentication methods—such as passwords, PINs, and even biometric systems (fingerprint, facial recognition)—typica... Federated learning lets many clients collaboratively train a shared model without exchanging raw data, making it ideal for priv... Alzheimer's disease (AD) is the most common form of dementia, affecting millions globally, yet early and accurate detection rem... Clinical decision-making demands the integration of heterogeneous data sources—lab results, imaging, clinical notes, vital sign... Predicting patient outcomes in the Intensive Care Unit (ICU)—mortality risk, length of stay, and deterioration events—can save ... Large language models are reshaping how clinical knowledge is accessed, explained, and acted upon—but healthcare is precisely w... Deep learning now matches clinicians on many diagnostic and screening tasks, yet adoption stalls when models cannot explain the... Bibliometric overview of InfoLab's scholarly output and influence. Defending AI against adversarial attacks, malware, binary analysis, and exploitat