Professor Hong Joo-hwa Appears on SBS Program

Professor Hong Joo-hwa Appears on SBS Program <Unanswered Questions> URL: https://programs.sbs.co.kr/culture/unansweredquestions/clip/55073/22000592156

Three Short Papers accepted at TheWebConf (WWW) 2025 from Professor Simon S. Woo’s Lab (DASH Lab)

The Data-driven AI & Security HCI Lab (DASH Lab, Advisor: Simon S. Woo) has had three short papers accepted for publication at the International World Wide Web Conference (WWW), a top-tier international conference in BK Computer Science, covering web technologies, internet advancements, data science, and artificial intelligence. The papers will be presented in April in Sydney, Australia. 1. Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale Dataset, WWW 2025 Authors:Muhammad Shahid Muneer (Ph.D. Student, Department of Software), Simon S. Woo (Professor, Department of Software, Sungkyunkwan University) 2. Fairness and Robustness in Machine Unlearning, WWW 2025 Authors: Khoa Tran (Integrated M.S./Ph.D. Student, Department of Software), Simon S. Woo (Professor, Department of Software, Sungkyunkwan University) Machine unlearning addresses the challenge of removing the influence of specific data from a pretrained model, which is a crucial issue in privacy protection. While existing approximated unlearning techniques emphasize accuracy and time efficiency, they fail to achieve exact unlearning. In this study, we are the first to incorporate fairness and robustness into machine unlearning research. Our study analyzes the relationship between fairness and robustness based on fairness conjectures, and experimental results confirm that a larger fairness gap makes the model more vulnerable. Additionally, we demonstrate that state-of-the-art approximated unlearning methods are highly susceptible to adversarial attacks, significantly degrading model performance. Therefore, we argue that fairness-gap measurement and robustness metrics should be essential evaluation criteria for unlearning algorithms. Finally, our findings show that unlearning at the intermediate and final layers is sufficient while also improving time and memory efficiency. 3. SADRE: Saliency-Aware Diffusion Reconstruction for Effective Invisible Watermark Removal, WWW 2025 Authors: Inzamamul Alam (Ph.D. Student, Department of Software), Simon S. Woo (Professor, Department of Software, Sungkyunkwan University) To address the robustness limitations of existing watermarking techniques, this study proposes SADRE (Saliency-Aware Diffusion Reconstruction), a novel watermark removal framework. SADRE applies saliency mask-guided noise injection and diffusion-based reconstruction to preserve essential image features while effectively removing watermarks. Additionally, it adapts to varying watermark strengths through adaptive noise adjustment and ensures high-quality image restoration via a reverse diffusion process. Experimental results demonstrate that SADRE outperforms state-of-the-art watermarking techniques across key performance metrics, including PSNR, SSIM, Wasserstein Distance, and Bit Recovery Accuracy. This research establishes a theoretically robust and practically effective watermark removal solution, proving its reliability for real-world web content applications.

One paper accepted at EuroS&P 2025 from Professor Simon S Woo's (DASH Lab)

The Data-driven AI & Security HCI Lab (DASH Lab, Advisor: Simon S. Woo) has had one System of Knowledge (SoK) paper accepted for publication at the 10th IEEE European Symposium on Security and Privacy (Euro S&P), a prestigious international conference covers Machine Learning Security, System & Network Security, Cryptographic Protocols, Data Privacy. The papers will be presented in July in Venice, Italy. SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework, EuroS&P 2025 Authors: Binh Le and Jiwon Kim (Ph.D. Student, Department of Software), Simon S. Woo (Professor, Department of Software, Sungkyunkwan University) This work is jointly performed with CSIRO Data61 as an international collaboration. Paper Link: https://arxiv.org/abs/2401.04364

Professor Woo Hongwook’s Research Lab (CSI Lab), ICLR 2025 Paper Acceptance

[Professor Woo Hongwook’s Research Lab (CSI Lab), ICLR 2025 Paper Acceptance] Two papers from CSI Lab (Supervised by Professor Woo Hongwook) have been accepted for presentation at ICLR 2025 (The 13th International Conference on Learning Representations), a prestigious conference in the field of Artificial Intelligence. The papers will be presented in April 2025 at the Singapore Expo in Singapore. 1. Paper “Model Risk-sensitive Offline Reinforcement Learning” The author of this paper is Kwangpyo Yoo, a Ph.D. candidate in the Department of Software. This study proposes a Model Risk-sensitive Reinforcement Learning (Model Risk-sensitive RL) framework for critical mission domains, such as robotics and finance, where decision-making is crucial. The paper particularly details a model risk-sensitive offline reinforcement learning technique (MR-IQN). MR-IQN aims to minimize the "model risk" loss in cases where the model's learned data differs from the real environment, leading to decreased accuracy. To achieve this, it calculates the model's confidence in each data point and evaluates the model risk per data point using a Critic-Ensemble Criterion. It also introduces a Fourier Feature Network that limits the gap between the actual policy's value function and the inferred policy’s value in an offline setting. MR-IQN outperformed other state-of-the-art risk-sensitive reinforcement learning techniques in experiments conducted in MT-Sim (financial trading environment) and AirSim (autonomous driving simulator), achieving lower risk and higher average performance. 2. Paper “NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains” This paper was co-authored by Wonje Choi (Ph.D. candidate, Department of Software), Jinwoo Park (Master’s student, Department of Artificial Intelligence), Sanghyun Ahn (Master’s student, Department of Software), and Daehui Lee (Integrated Master’s and Ph.D. candidate). The study proposes a Neuro-symbolic Continual Learner (NeSyC) framework that continuously generalizes knowledge (Actionable Knowledge) from embodied experiences to be applied to various tasks in open-domain physical environments. NeSyC mimics the human cognitive process of hypothesizing and deducing (hypothetico-deductive reasoning) to improve performance in open domains. This is achieved by: Using LLMs and symbolic tools to repeatedly generate and verify hypotheses from acquired experiences in a contrastive generality improvement approach. Utilizing memory-based monitoring to detect action errors of embodied agents in real-time and refine their knowledge, ultimately improving the agent's task performance and generalization across open-domain environments. NeSyC was evaluated across various benchmark environments, including ALFWorld, VirtualHome, Minecraft, RLBench, and real-world robotic tabletop scenarios. It demonstrated robust performance across dynamic open-domain environments and outperformed state-of-the-art methods, such as AutoGen, ReAct, and CLMASP, in task success rates. CSI Lab conducts research on network and cloud system optimization, autonomous driving of robots and drones, and other self-learning technologies by leveraging Embodied Agent, Reinforcement Learning, and Self-Learning. Contact Information:Professor Woo Hongwook | hwoo@skku.edu | CSI Lab | https://sites.google.com/view/csi-agent-group