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- [Research] CSI Lab (Prof. Honguk Woo) – Two Papers Accepted for IROS 2026 and ECCV 2026
- CSI연구실(지도교수: 우홍욱,https://csiagentgroup.com)의 논문이 로봇공학 분야 우수학회인 IROS 2026 (IEEE/RSJ International Conference on Intelligent Robots and Systems)과 인공지능 분야 우수학회인 ECCV 2026 (The 19th European Conference on Computer Vision)에 게재 승인(Accept) 되었습니다. IROS 논문은 26년 9월 미국 펜실베이니아주 피츠버그의 David L. Lawrence Convention Center에서 발표될 예정이며, ECCV 논문은 26년 9월 스웨덴 말뫼의 Malmö Arena와 Malmömässan에서 발표될 예정입니다. 1. [IROS] 논문 “ROBOBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents”는 소프트웨어학과 윤시형(석박통합과정), 유민종(삼성리서치, 박사), 안상현(석사과정) 연구원이 저자로 참여했습니다. 본 연구에서는 Vision-Language-Action(VLA) 모델과 같은 행동 생성 정책을 실제 로봇 환경에서 강건한 에이전트로 확장하는 ROBOBRIDGE 프레임워크를 제안합니다. 기존 VLA 모델은 행동 예측에는 효과적이지만 실패 복구, 장기 작업 수행, 관측·작업·로봇 변화에 대한 강건성 측면에서 한계를 가지고 있습니다. ROBOBRIDGE는 정책 모델을 재학습하지 않고 외부 orchestration 계층으로 감싸 이러한 한계를 보완합니다. ROBOBRIDGE는 Monitor, Perceptor, Planner, Controller, Robot Interface의 다섯 개 모듈로 이루어져 있습니다. Perceptor는 관찰을 객체 중심 상태로 변환하고, Planner는 현재 장면과 언어 지시를 바탕으로 primitive plan을 생성합니다. Controller는 VLA, IK solver, scripted policy 등 실제 동작을 생성하는 정책이 들어가는 부분이며, Robot Interface는 Franka Research 3, UR7e와 같은 서로 다른 로봇 플랫폼의 API와 좌표계를 추상화합니다. Monitor는 실행 결과를 확인하고 retry, regenerate, replan, re-perceive 등 계층적 복구 전략을 선택합니다. 핵심적으로 ROBOBRIDGE는 실패 복구를 위한 two-phase monitoring과 장기 작업 수행을 위한 reactive planning with asynchronous perception을 결합합니다. 실패가 감지되면 원인을 진단해 필요한 복구 단계를 선택하고, 환경이 바뀌면 최신 객체 상태를 반영해 이후 primitive를 다시 생성합니다. VLA를 Controller로 사용할 때에는 primitive skill별 LoRA adapter를 적용하는 primitive skill fine-tuning을 통해 domain shift에 대한 민감도를 완화합니다. LIBERO 및 RoboCasa 시뮬레이션 환경과 실제 Franka Research 3, UR7e 로봇 플랫폼에서의 실험 결과, ROBOBRIDGE는 standalone VLA 및 기존 augmented VLA deployment 대비 일관된 성능 향상을 보였습니다. 이러한 결과는 안정적인 로봇 에이전트가 단순한 action predictor가 아니라 perception, planning, monitoring, recovery가 결합된 실행 구조를 통해 구현될 수 있음을 보여 줍니다. 2. [ECCV]논문 “Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments”은 소프트웨어학과 장진우(석박통합과정), 윤시형(석박통합과정) 연구원과 인공지능학과 노정호(석사과정), 조현석(석박통합과정) 연구원이 저자로 참여했습니다. 본 연구에서는 체화형 에이전트(embodied agents)가 지속적으로 변하는 환경에서 관찰 데이터만으로 적응할 수 있게 하는 MuSix(Multi-Scale Mixture of World Models) 프레임워크를 제안합니다. 이 프레임워크는 기존 월드 모델에 MoE 구조를 접목하면서 물리적 스케일의 월드 모델과 추상적 스케일의 월드 모델 등 다양한 스케일의 월드 모델을 혼합하는 방법과 이들이 유기적으로 진화하도록 지식을 주입하는 방법을 제안합니다. MuSix는 월드 모델 혼합 부분과 월드 모델 진화 부분으로 이루어져 있습니다. 월드 모델 혼합 부분은 스케일에 따라 라우팅 방식을 조절하는 meta-router와 실제 월드 모델을 라우팅하는 라우터의 2단계 구조로 이루어져 있습니다. 월드 모델 진화 부분에서는 새로운 지식이 스케일 단위로 그룹화된 월드 모델 그룹 각각에 주입되면서 필요하지 않은 기존 지식을 제거하는 intra-scale evolution이 일어나고, 동시에 스케일 간에 지식을 전이하는 inter-scale evolution이 발생합니다. EmbodiedBench 및 실제 Franka 로봇 조작 환경에서의 실험 결과 MuSix는 기존 SayCanPay 대비 평균 14.51% 높은 작업 성공률을 달성했습니다. 이러한 결과는 MuSix가 지속적으로 변하는 환경에서 체화형 에이전트가 스케일 단위로 월드 모델을 혼합하고 진화하게 하여 실시간으로 적응할 수 있게 하며, 이를 통해 지속적인 환경 변화에도 강건하게 추론할 수 있음을 보여 줍니다. CSI 연구실은 Embodied AI, Vision-Language-Action Model, Reinforcement Learning 기술을 활용하여 범용 인공지능 에이전트 연구와 로봇 지능 연구 등을 수행하고 있습니다. 우홍욱 | hwoo@skku.edu | CSI Lab | https://csiagentgroup.com
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- 작성일 2026-06-23
- 조회수 676
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- [Research] One paper accepted at DFRWS USA 2026 from Professor Sungjae Hwang 's Softsec Lab
- 소프트웨어 보안 연구실(지도교수 황성재, https://softsec.skku.edu/)의 윤진희 학생(석사과정)이 수행한 연구 논문 “Forensic Analysis of Video Data Deletion and Recovery in Honeywell Surveillance File System”이 디지털 포렌식 분야의 저명한 국제 학회인 Digital Forensics Research Conference USA 2026 (DFRWS USA ’26)에 게재 승인되었습니다. 해당 논문은 2026년 7월 미국에서 발표될 예정이며, 국내 대학 소속 연구팀으로는 유일하게 DFRWS USA ’26에 논문이 채택되었다는 점에서 의미가 큽니다. 실제 범죄 현장에서의 실시간 감시 비디오는 객관적이고 신뢰도 높은 결정적 증거로 활용되며, 다양한 사건의 증거로 활용되어 왔다. 하지만 이러한 특성에 따라 범죄자가 증거 인멸을 목적으로 영상을 삭제하거나, 저장 장치를 물리적으로 망가뜨리는 증거 인멸 시도 또한 수사 현장에서 빈번하게 발생한다. 본 연구는 이러한 증거 인멸 상황이 발생했을 때, 디지털 포렌식 수사관이 디지털 영상 녹화기(DVR) 및 네트워크 영상 녹화기(NVR)로부터 실제 영상 데이터를 빠르게 추출 및 복구할 수 있도록 돕는 것을 목적으로 한다. 전 세계 영상 보안 시장의 주요 공급사 중 하나인 Honeywell 제조사는 북미 영상 감시 시장의 핵심 공급사임에도 불구하고 내부 파일 시스템 구조가 공개되지 않았으며, 이는 수사관으로 하여금 인멸 시도가 발생했을 때 분석을 어렵게 하는 원인이 된다. 이러한 문제 해결을 위해 본 연구에서는 Binary Diffing 기법을 활용하여 Honeywell NVR 파일 시스템의 내부 구조를 분석하고, 핵심 영역의 구조를 규명하여 Honeywell NVR 기기가 지원하는 세 가지 삭제 방식별 파일 시스템 메타데이터와 실제 데이터의 변화를 정밀 분석하였다.이후 Carving 기법을 통해 삭제된 영상을 성공적으로 복구할 수 있음을 실험으로 증명하였다. 이를 통해 범죄자가 증거 인멸을 시도하더라도 적절한 포렌식 분석을 통해 영상 증거를 되살릴 수 있음을 증명하였다. Abstract: Real-time video surveillance systems store recorded video using digital video recorders (DVRs) and network video recorders (NVRs). To support continuous high-volume video storage, these devices employ specialized, nonstandard file systems that are often proprietary and undocumented. This lack of documentation significantly increases the time and effort required for forensic analysis. In this study, we analyze an undocumented proprietary file system used by Honeywell video surveillance devices—one that, to the best of our knowledge, has not been examined in prior work—and investigate its deletion mechanisms and demonstrate the feasibility of video recovery after deletion. We perform a file system analysis using a binary diffing technique and evaluate three deletion methods supported by the target device: (1) formatting-based deletion, (2) data expiration, and (3) overwrite. For each method, we investigate changes in file system metadata and on-disk data structures and demonstrate the feasibility of video data recovery. Our findings aim to support more efficient and accurate forensic investigations of Honeywell surveillance products and provide foundational insights into the analysis of proprietary file systems used in video recording devices.
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- 작성일 2026-06-17
- 조회수 864
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- [Research] One paper accepted at ACL 2026 from Professor young Joong Ko's NLP Lab
- 자연어처리연구실(NLP lab, 지도교수: 고영중)의 논문 1편이 인공지능 및 자연어처리 분야의 Top-tier 국제학술대회인 ACL 2026 (The 64th Annual Meeting of the Association for Computational Linguistics)의 Findings에 게재되었습니다. 논문: ConvX: A Lightweight Converter to Bridge Indexed Dense Representations and Large Language Models for Retrieval-Augmented Generation (인공지능학과 박사과정 최봉근, 인공지능학과 박사과정 김근하, 인공지능학과 석박사통합과정 한준호) 논문 요약: 본 연구에서는 RAG 파이프라인의 치명적인 효율성 문제와, 이를 해결하기 위한 기존 압축 기반 방법론들이 검색된 문맥을 다시 인코딩하며 발생하는 이중 인코딩(double-encoding) 문제를 해소하기 위해, 검색기가 생성한 색인된 밀집 표현(dense representation)을 직접 활용하여 긴 텍스트 문맥을 전적으로 대체하는 효과적인 압축 기반 RAG 프레임워크인 ConvX를 제안합니다. 제안한 방법은 경량 변환기(converter)를 통해 단일 밀집 표현을 고정된 수의 메모리 슬롯으로 확장합니다. 메모리 슬롯으로부터 문단 수준의 어휘 정보를 복원하도록 변환기를 학습합니다. 변환기를 통해 생성된 메모리 슬롯은 LLM의 기존 토큰 임베딩과 특성이 상이하므로, LLM이 메모리 슬롯에 대한 내용을 복원할 수 있도록 사전학습을 수행합니다. 이때, 다중 문서의 RAG 시스템에 적용할 수 있도록 단일 및 다중 문단 메모리 슬롯을 복원하도록 학습합니다. 이러한 설계는 입력 길이와 계산 오버헤드를 크게 줄이면서도 효율적인 지식 통합을 가능하게 합니다. 실험 결과, 제안한 모델은 RAG 환경에서 기존의 ad-hoc 문맥 압축 기법들 대비 우수한 성능을 달성하는 동시에, 추론 효율성을 크게 향상시킴을 확인하였습니다. Abstract: Retrieval-Augmented Generation (RAG) has significantly advanced open-domain question answering and dialogue systems by incorporating external knowledge into large language models. Despite its effectiveness, existing RAG pipelines suffer from critical efficiency limitations. In particular, modern transformer-based generators exhibit quadratic or higher computational complexity with respect to input sequence length and hidden dimensionality, leading to substantial inference latency as model scales and contextual inputs increase. This issue is exacerbated in RAG settings, where retrieved contexts substantially expand the input prompt. To alleviate this challenge, we propose an effective compression-based RAG framework, ConvX, that directly leverages indexed dense representations produced by a retriever, entirely substituting to long text contexts. Our approach expands a single dense representation into a fixed number of memory slots using a lightweight converter to provide rich lexical information. This design enables efficient knowledge integration while significantly reducing input length and computational overhead. Empirical evaluations demonstrate that the proposed model achieves outstanding performances compared to existing ad-hoc context compression methods in RAG setting, while offering substantially improved inference efficiency. 고영중 교수: yjko@skku.edu, nlp.skku.edu, 자연어처리연구실: nlplab.skku.edu
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- 작성일 2026-06-08
- 조회수 1423
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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 2026-05-22
- 조회수 1577
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- [Research] Security Engineering Laboratory (SecLab) under Professor Kim Hyung-sik – Paper Accepted for Publication at the S&P 2025
- Security Engineering Laboratory (SecLab) at SKKU (Advisor: Kim Hyung-sik, https://seclab.skku.edu) – "Open Sesame! On the Security and Memorability of Verbal Passwords" Accepted for IEEE Symposium on Security and Privacy (S&P) 2025 The paper "Open Sesame! On the Security and Memorability of Verbal Passwords," conducted by Ph.D. candidate Kim Eun-soo and Professor Kim Hyung-sik at the Security Engineering Laboratory, has been accepted for publication at the IEEE Symposium on Security and Privacy (S&P) 2025, one of the most prestigious conferences in the field of computer security. The study was conducted in collaboration with Professor Kim Doo-won of the University of Tennessee and alumnus Lee Ki-ho from the Security Engineering Laboratory (currently at ETRI). The research quantitatively analyzed the security and memorability of verbal passwords through two large-scale user experiments, demonstrating that verbal passwords offer a practical and secure alternative to traditional text-based passwords by overcoming their inherent limitations. In the first user experiment, verbal passwords freely generated by 2,085 participants were evaluated for both short-term and long-term memorability as well as security. Security testing conducted using the PassphraseGPT model—trained on over 20 million common English phrases—revealed that approximately 39.76% of the user-generated verbal passwords could be predicted within one billion guess attempts. In the second experiment, involving 600 participants, a password creation policy that enforced a minimum word count and incorporated a blocklist was implemented. This approach significantly improved security while maintaining ease of memorability. In long-term memory tests, 65.6% of users in the verbal password group were able to successfully recall their passwords, compared to 54.11% for text-based passwords. Moreover, the proportion of verbal passwords susceptible to guessing attacks was lower than that of text passwords, indicating a stronger resistance to such attacks. This research has been highly acclaimed for demonstrating that verbal passwords provide a practical and secure alternative to text-based passwords in scenarios where keyboard input is either impossible or inconvenient—such as with smart assistants, wearable devices, in-vehicle systems, and VR/AR environments. The study will be presented in May 2025 in San Francisco, California, USA. Abstract Despite extensive research on text passwords, the security and memorability of verbal passwords—spoken rather than typed—remain underexplored. Verbal passwords hold significant potential for scenarios where keyboard input is impractical (e.g., smart speakers, wearables, vehicles) or users have motor impairments that make typing difficult. Through two large-scale user studies, we assessed the viability of verbal passwords. In our first study (N = 2,085), freely chosen verbal passwords were found to have a limited guessing space, with 39.76% cracked within 10^9 guesses. However, in our second study (n = 600), applying word count and blocklist policies for verbal password creation significantly enhanced verbal password performance, achieving better memorability and security than traditional text passwords. Specifically, 65.6% of verbal password users (under the password creation policy using minimum word counts and a blocklist) successfully recalled their passwords in long-term tests, compared to 54.11% for text passwords. Additionally, verbal passwords with enforced policies exhibited a lower crack rate (6.5%) than text passwords (10.3%). These findings highlight verbal passwords as a practical and secure alternative for contexts where text passwords are infeasible, offering strong memorability with robust resistance to guessing attacks.
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- 작성일 2025-04-29
- 조회수 8789
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- [Research] Security Engineering Laboratory (Advisor: Kim Hyung-sik) – Two Papers Accepted for Oral Sessions at The Web Conference
- The Security Engineering Laboratory, under the supervision of Professor Kim Hyung-sik, in collaboration with Professor Kim Doo-won from the University of Tennessee, has had two research papers accepted for oral sessions at The Web Conference (WWW) 2025, one of the premier international conferences in the web domain. In this research, alumnus Lee Ki-ho, a former member of the Security Engineering Laboratory (currently at ETRI), participated as a visiting researcher at the University of Tennessee and collaborated with Professor Kim Hyung-sik. Both papers, based on extensive empirical data, quantitatively analyze the characteristics and structures of phishing attacks. They have been highly acclaimed for providing a fundamental understanding of phishing attacks and proposing new countermeasures. The presentations are scheduled to take place in May 2025 in Sydney, Australia. Paper 1. 7 Days Later: Analyzing Phishing-Site Lifespan After DetectedThis paper presents an empirical study analyzing the lifetime and evolution of phishing sites after detection. Over a period of five months, 286,237 phishing URLs were tracked at 30-minute intervals to examine the attack patterns of phishing sites, shedding light on why the effectiveness of conventional phishing detection strategies is diminishing. Phishing sites have a short lifespan—with an average survival time of 54 hours and a median of 5.46 hours—highlighting the limitations of training and detection approaches. For instance, Google Safe Browsing detects phishing sites, on average, 4.5 days after their emergence; however, 84% of phishing sites cease operations before detection, demonstrating the inherent limitations of such detection methods. Paper 2. What's in Phishers: A Longitudinal Study of Security Configurations in Phishing Websites and Kits This paper presents a systematic analysis of phishing infrastructure by comprehensively examining the security configurations and structural vulnerabilities based on a combined dataset of 906,731 phishing websites and 13,344 phishing kits collected over a period of 2 years and 7 months. The study has attracted attention for proposing a proactive strategy that leverages the structural weaknesses of phishing sites to neutralize the attack infrastructure, thereby moving away from traditional passive detection and blocking methods and towards an early shutdown approach for phishing sites.
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- 작성일 2025-04-29
- 조회수 8740
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- [Research] IEEE S&P 2025 Paper Acceptance Announcement from Professor Lee Ho-jun’s Research Laboratory (SSLab)
- [IEEE S&P 2025 Acceptance Announcement – SSLab, Professor Hojoon Lee] The paper from the System Security Laboratory (SSLab), under the supervision of Professor Hojoon Lee, has been accepted for publication at IEEE S&P 2025, one of the four premier international conferences in the security field. The paper is scheduled for presentation in May in San Francisco, California, USA. Title: IncognitOS: A Practical Unikernel Design for Full-System Obfuscation in Confidential Virtual Machines Authors: Kha Dinh Duy, Jaeyoon Kim, Hajeong Lim, Hojoon Lee Summary: Recent works have repeatedly proven the practicality of side-channel attacks in undermining the confidentiality guarantees of Trusted Execution Environments such as Intel SGX. Meanwhile, the trusted execution in the cloud is witnessing a trend shift towards confidential virtual machines (CVMs). Unfortunately, several side-channel attacks have survived the shift and are feasible even for CVMs, along with the new attacks discovered on the CVM architectures. Previous works have explored defensive measures for securing userspace enclaves (i.e., Intel SGX) against side-channel attacks. However, the design space for a CVM-based obfuscation execution engine is largely unexplored. This paper proposes a unikernel design named IncognitOS to provide full-system obfuscation for CVM-based cloud workloads. IncognitOS fully embraces unikernel principles such as minimized TCB and direct hardware access to render full-system obfuscation feasible. IncognitOS retrofits two key OS components, the scheduler and memory management, to implement a novel adaptive obfuscation scheme. IncognitOS's scheduling is designed to be self-sovereign from the timer interrupts from the untrusted hypervisor with its synchronous tick delivery. This allows IncognitOS to reliably monitor the frequency of the hypervisor's possession of execution control (i.e., VMExits) and adjust the frequency of memory rerandomization performed by the paging subsystem, which transparently performs memory rerandomization through direct MMU access. The resulting IncognitOS design makes a case for self-obfuscating unikernel as a secure CVM deployment strategy while further advancing the obfuscation technique compared to previous works. Evaluation results demonstrate IncognitOS's resilience against CVM attacks and show that its adaptive obfuscation scheme enables practical performance for real-world programs.
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- 작성일 2025-04-29
- 조회수 8739
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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 2025-03-05
- 조회수 7494
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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 2025-03-05
- 조회수 7283
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