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- [Research] One paper accepted at ACM KDD 2026 from Professor Tamer’s InfoLab
- Professor Tamer’s InfoLab has had a paper accepted for presentation at ACM KDD 2026, a premier international conference in data science, AI, knowledge discovery, and data mining, to be held from August 9–13, 2026, in Jeju, South Korea. Figure 1 Example advantages of VisionDES over static ensemble models. Models with red highlights are attacked models. The accepted paper, titled “VisionDES: Robust and Explainable Dynamic Vision Ensemble,” introduces the first dynamic ensemble selection framework for vision tasks. VisionDES uses deep vision embeddings and approximate nearest-neighbor search to identify a local region of competence for each test image, then dynamically selects and weights the most reliable models for the final predictions. The method is designed to improve robustness under adversarial attacks and distribution shifts while providing novel instance-level interpretability. Figure 2 Framework of the proposed VisionDES, consisting of three main stages: training, selection, and aggregation. The paper reports extensive evaluations on several image datasets under clean conditions, adversarial attacks, and distribution shifts. VisionDES outperforms static ensembles and uncertainty-based dynamic ensemble methods, achieving up to 20% higher robust accuracy under strong attacks and 2–3% higher accuracy under distribution shifts. Figure 3 Interpretability for test images under benign (top) and adversarial (bottom) conditions. We show each model’s behavior in the Region of Competence (RoC), predictions, and RoC samples with their L2 distances (computed via FAISS). VisionDES strengthens trustworthy computer vision by making ensemble models more adaptive, more robust to adversarial attacks and distribution shifts, and more explainable at the level of individual predictions. For more details about InfoLab research activities, visit https://infolab.skku.edu
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- 작성일 2026-05-20
- 조회수 267
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- [Research] 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.
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- 작성일 2025-03-04
- 조회수 3554
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- [Research] 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
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- 작성일 2025-03-01
- 조회수 3588
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- [Research] [Prof. Hyoungshick Kim] secLab, Wins Top Prize in Financial Security Institute
- [Prof. Hyoungshick Kim] secLab, Wins Top Prize in Financial Security Institute ▲ (From left) Researcher Sang-Yoon Seok, President Chul-Woong Kim of the Financial Security Agency, Graduate Student Hyunmin Choi, and Student Jihoon Kim Jihoon Kim and Hyunmin Choi, members of the Security Engineering Lab (supervised by Professor Hyungshick Kim) in the Department of Electrical, Electronic, and Computer Engineering, collaborated with Sang-Yoon Seok, a researcher at Naver Cloud, to win the top prize at the 8th Financial Security Institute’s Paper Contest. The award ceremony was held on Thursday, November 7, at the Conrad Hotel in Yeouido, Seoul. Hyunmin Choi is currently conducting research on privacy protection at Naver Cloud. The annual paper competition, hosted by the Financial Security Institute, invites submissions on topics such as changes in the financial environment, new technologies, and improvements to laws and regulations. Eight outstanding papers are selected each year, and winners receive preferential benefits when applying to the Financial Security Institute. Hyunmin Choi, the corresponding author and a doctoral candidate in the Department of Computer Science and Engineering, stated, “With the mandatory use of financial MyData APIs, the importance of data privacy technology is increasing. Our paper focused on enhancing security through homomorphic encryption and enabling data combination technologies.” Jihoon Kim, the first author and an undergraduate student in the Department of Mathematics, shared, “This research was a valuable learning experience, and I hope to continue contributing to advancements in security technology.” Professor Hyungshick Kim added, “This project provided students with a meaningful opportunity to apply the latest security technologies in real-world settings through collaboration with Naver Cloud.”
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- 작성일 2024-11-21
- 조회수 3730
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- [Research] [Prof. Sooyoung Cha] SALab, Papers Approved for Publication at the ICSE 2025 International Conference
- [Prof. Sooyoung Cha] SALab, Papers Approved for Publication at the ICSE 2025 International Conference ■ Title: TopSeed: Learning Seed Selection Strategies for Symbolic Execution from Scratch ■ Author of a paper: Jaehyeok Lee, Prof. Sooyoung Cha ■ Conference: IEEE/ACM International Conference on Software Engineering (ICSE 2025) ■ Abstract: We present TopSeed, a new approach that automatically selects optimal seeds to enhance symbolic execution. Recently, the performance of symbolic execution has significantly improved through various state-of-the-art techniques, including search strategies and state-pruning heuristics. However, these techniques have typically demonstrated their effectiveness without considering “seeding”, which efficiently initializes program states for exploration. This paper aims to select valuable seeds from candidate inputs generated during interactions with any symbolic execution technique, without the need for a predefined seed corpus, thereby maximizing the technique's effectiveness. One major challenge is the vast number of candidates, making it difficult to identify promising seeds. To address this, we introduce a customized online learning algorithm that iteratively groups candidate inputs, ranks each group, and selects a seed from the top-ranked group based on data accumulated during symbolic execution. Experimental results on 17 open-source C programs show that TopSeed significantly enhances four distinct cutting-edge techniques, implemented on top of two symbolic executors, in terms of branch coverage and bug-finding abilities.
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- 작성일 2024-11-04
- 조회수 2950
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- [Research] [prof.Simon S. Woo] DASH Lab, Two Papers Approved for Publication at the CIKM 2024 International Conference
- [prof.Simon S. Woo] DASH Lab, Two Papers Approved for Publication at the CIKM 2024 International Conference 1. IDENTIFY: Integral Radial and Spatial Fourier Analysis for AI-Generated Image Authentication (full paper) Writer: Inzamamul Alam, Muhammad Shahid Muneer, and Prof. Simon S. Woo This study proposes a method for detecting deepfakes generated by a new generative AI (Diffusion) technique using Integral Radial and Spatial Fourier Analysis with high performance. Notably, the proposed method shows 12-28% higher performance compared to existing approaches. IDENTIFY: Integral Radial and Spatial Fourier Analysis for AI-Generated Image Authentication, Inzamamul Alam, Muhammad Shahid Muneer, and Simon S. Woo*, 33rd ACM International Conference on Information & Knowledge Management (CIKM), Boise, Idaho, USA, October 2024 2. Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital Marketing (full paper) Writer: Girim Ban, Hyeonseok Yun, Banseok Lee, David Sung, and Prof. Simon S. Woo This study proposes Deep Journey Hierarchical Attention Networks (DJHAN) to enhance user conversion prediction in digital marketing, improving key metrics such as Conversion Rate (CVR) and Return on Ad Spend (ROAS) compared to existing methods. The proposed model demonstrated high performance when applied to real marketing data from KT/NasMedia. Deep Journey Hierarchical Attention Networks for Predictions in Digital Marketing Girim Ban, Hyeonseok Yun, Banseok Lee, David Sung, and Simon S. Woo* 33rd ACM International Conference on Information & Knowledge Management (CIKM), Boise, Idaho, USA, October 2024
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- 작성일 2024-08-29
- 조회수 3541
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- [Research] DASH Lab won the Best Paper Running-Up Award (2nd Best Paper) at PAKDD 2024 in Taiwan
- Binh M. Le and Simon S. Woo’s paper, “SEE: Spherical Embedding Expansion for Improving Deep Metric Learning,” received the the Best Paper Running-Up Award (2nd best paper) in PAKDD 2024 (BK CS IF=1), held in Taipei in May 2024. Here is the background information about the award: “This year, PAKDD received 720 excellent submissions, and the selection process was competitive, rigorous, and thorough with over 500 PC and 100 SPC members. An award committee was formed by a chair and four committee members from different countries. There are only one Best Paper Award, two Best Paper Running-Up Awards, and one Best Student Paper Award.” Paper Link: https://link.springer.com/chapter/10.1007/978-981-97-2253-2_11 https://pakdd2024.org/award24awardpakdd24/
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- 작성일 2024-06-07
- 조회수 4159
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- [Research] [Prof. Koo,Hyung joon/ Prof. Hwang, sung jae] SecAI/SoftSec Lab, Approval for FSE2024 publication
- [Prof. Koo,Hyung joon/ Prof. Hwang, sung jae] SecAI/SoftSec Lab, Approval for ACM International Conference on the Foundations of Software Engineering (FSE 2024) publication Abstract. Decompilation is a process of converting a low-level machine code snippet back into a high-level programming language such as C. It serves as a basis to aid reverse engineers in comprehending the contextual semantics of the code. In this respect, commercial decompilers like Hex-Rays have made significant strides in improving the readability of decompiled code over time. While previous work has proposed the metrics for assessing the readability of source code, including identifiers, variable names, function names, and comments, those metrics are unsuitable for measuring the readability of decompiled code primarily due to i) the lack of rich semantic information in the source and ii) the presence of erroneous syntax or inappropriate expressions. In response, to the best of our knowledge, this work first introduces R2I, the Relative Readability Index, a specialized metric tailored to evaluate decompiled code in a relative context quantitatively. In essence, R2I can be computed by i) taking code snippets across different decompilers as input and ii) extracting pre-defined features from an abstract syntax tree. For the robustness of R2I, we thoroughly investigate the enhancement efforts made by existing decompilers and academic research to promote code readability, identifying 31 features to yield a reliable index collectively. Besides, we conducted a user survey to capture subjective factors such as one’s coding styles and preferences. Our empirical experiments demonstrate that R2I is a versatile metric capable of representing the relative quality of decompiled code (e.g., obfuscation, decompiler updates) and being well aligned with human perception in our survey.
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- 작성일 2024-01-25
- 조회수 4088
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- [Research] [Prof. SHIN, DONG KUN] Intelligent Embedded Systems Lab, AAAI 2024 Conference Paper
- [Prof. SHIN, DONG KUN] Intelligent Embedded Systems Lab, AAAI 2024 Conference Paper [Paper #1 Information] Proxyformer: Nystrom-Based Linear Transformer with Trainable Proxy Tokens Sangho Lee, Hayun Lee, Dongkun Shin Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024 Transformer-based models have demonstrated remarkable performance in various domains, including natural language processing, image processing and generative modeling. The most significant contributor to the successful performance of Transformer models is the self-attention mechanism, which allows for a comprehensive understanding of the interactions between tokens in the input sequence. However, there is a well-known scalability issue, the quadratic dependency of self-attention operations on the input sequence length n, making the handling of lengthy sequences challenging. To address this limitation, there has been a surge of research on efficient transformers, aiming to alleviate the quadratic dependency on the input sequence length. Among these, the Nyströmformer, which utilizes the Nyström method to decompose the attention matrix, achieves superior performance in both accuracy and throughput. However, its landmark selection exhibits redundancy, and the model incurs computational overhead when calculating the pseudo-inverse matrix. We propose a novel Nyström method-based transformer, called Proxyformer. Unlike the traditional approach of selecting landmarks from input tokens, the Proxyformer utilizes trainable neural memory, called proxy tokens, for landmarks. By integrating contrastive learning, input injection, and a specialized dropout for the decomposed matrix, Proxyformer achieves top-tier performance for long sequence tasks in the Long Range Arena benchmark.
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- 작성일 2023-12-20
- 조회수 3702
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- [Research] A paper from Prof. Jinkyu Lee’s Lab. (RTCL@SKKU) published in IEEE RTSS 2023
- A paper from RTCL@SKKU (Advisor: Jinkyu Lee) has been published in IEEE RTSS 2023. IEEE RTSS is the premier conference in real-time systems, in which around 30 papers are usually published every year. In this year, IEEE RTSS 2023 was held in Taipei, Taiwan. IEEE RTSS 2023 Website http://2023.rtss.org/ Real-Time Computing Lab. Website https://rtclskku.github.io/website/ (Paper Title) RT-Blockchain: Achieving Time-Predictable Transactions (Abstract) Although blockchain technology is being increasingly utilized across various fields, the challenge of providing timing guarantees for transactions remains unmet, which is an obstacle in implementing blockchain solutions for time-sensitive applications such as high-frequency trading and real-time payments. In this paper, we propose the first solution to achieve a timing guarantee on blockchain. To this end, we raise and address two issues for timely transactions on a blockchain: (a) architectural support, and (b) real-time scheduling principles spe- cialized for blockchain. For (a), we modify an existing blockchain network, offering an interface to preferentially select the transactions with the earliest deadlines. We then extend the blockchain network to provide the flexibility of the number of generated blocks at a single block time. Under such architectural supports, we achieve (b) with three steps. First, to resolve a discrepancy between a periodic request of a transaction-generating node and the corresponding arrival on a block-generating node, we translate the former into the latter, which eases the modeling of the transaction load imposed on the blockchain network. Second, we derive a schedulability condition of the modeled transaction load, which guarantees no missed deadline for all transactions under a work-conserving deadline-based scheduling policy. Last, we develop a lazy scheduling policy and its condition, which reduces the number of generated blocks without compromising the degree of timing guarantees for the work-conserving policy. By implementing RT-blockchain on top of an existing open- source blockchain project, we demonstrate the effectiveness of the proposed scheduling principles with architectural supports in not only ensuring timely transactions but also reducing the number of generating blocks. Jinkyu Lee | jinkyu.lee@skku.edu | RTCL@SKKU | https://rtclskku.github.io/website/
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- 작성일 2023-12-11
- 조회수 4041