arXiv subjects

Ji Won Yoon

Publications and source records attributed to Ji Won Yoon.

At least 19 recordsLinked to original sources

arXiv2026

PairWatch: Pair-Aware Anomaly Transformer for Detecting Adversarial Rendezvous in Public Satellite Catalogs

Documented adversarial rendezvous and proximity operations (RPO) appear in the public TLE catalog, yet existing TLE-based maneuver detectors treat each satellite in isolation - mistaking routine station-keeping for adversarial behavior and missing RPO entirely when the target stays passive. We argue that the right unit is the satellite pair and the right inductive bias is the kinematic symmetry between approach and recede. We instantiate both with PairWatch, a pair-aware AnomalyTransformer whose attention prior is augmented with a sign-symmetric geometric similarity over relative-motion features, and release an open benchmark of 359 windows from public Space-Track data spanning 11 documented RPO events, 100 Clohessy-Wiltshire synthetic adversarial windows, and a five-class hard-negative set culminating in the TerraSAR-X / TanDEM-X helix formation. Under the deployment-relevant criterion of a single threshold separating real RPO from operational negatives - a necessary property for catalog-scale screening - PairWatch is the only model that succeeds, with the property holding in 100% of bootstrap resamples versus 0-19.5% for three neural baselines and under 0.1% for four physics-informed detectors. A per-source analysis exposes a shortcut-learning failure mode of single-number AUROC: simpler baselines attain higher overall AUROC by shortcutting a synthetic-vs-formation contrast with no operational meaning; removing this single cell flips the test ranking entirely (LSTM-AD), and these baselines fail the security-relevant task the benchmark was built to measure.

cs.CR↗

arXiv2026

SPECTER: Covert Data Exfiltration from Air-Gapped Systems via USB Power Modulation

We present SPECTER, a post-compromise covert channel that uses host-side USB power control to exfiltrate information from air-gapped and network-segregated systems. A transmitter with administrative privileges encodes bits through USB power-management or device-state operations without placing the payload in USB data transfers. The resulting signals are recovered through three alternative receiver paths based on smartphone charging states, visible peripheral behavior, and near-field RF activity. Experiments on Windows 10 and Ubuntu 22.04 demonstrate the channel on commodity hardware, with charging-state signaling at 0.2-0.25 bit/s across Android and iOS devices and peripheral-state signaling at 1.0 bit/s using a USB mouse on Windows. RF reception is demonstrated using a software-defined radio with a loop antenna in direct contact with the USB cable. Channel availability and observable side effects depend on the host configuration, connected device, and receiver path. These results identify host-side USB power control as a mechanism for low-rate exfiltration of short secrets and motivate treating USB power-management and device-state events as security-relevant telemetry.

cs.CR↗

arXiv2026

KoALa-Bench: Evaluating Large Audio Language Models on Korean Speech Understanding and Faithfulness

Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LALMs remain scarce for non-English languages, with Korean being one such underexplored case. In this paper, we introduce KoALa-Bench, a comprehensive benchmark for evaluating Korean speech understanding and speech faithfulness of LALMs. In particular, KoALa-Bench comprises six tasks. Four tasks evaluate fundamental speech understanding capabilities, including automatic speech recognition, speech translation, speech question answering, and speech instruction following, while the remaining two tasks evaluate speech faithfulness, motivated by our observation that several LALMs often fail to fully leverage the speech modality. Furthermore, to reflect Korea-specific knowledge, our benchmark incorporates listening questions from the Korean college scholastic ability test as well as content covering Korean cultural domains. We conduct extensive experiments across six models, including both white-box and black-box ones. Our benchmark, evaluation code, and leaderboard are publicly available at https://ksbench.github.io/Korean-Benchmark/.

cs.CL↗

arXiv2026

Semantic-Aware Reconstruction Error for Detecting AI-Generated Images

Recently, AI-generated image detection has gained increasing attention, as the rapid advancement of image generation technologies has raised serious concerns about their potential misuse. While existing detection methods have achieved promising results, their performance often degrades significantly when facing fake images from unseen, out-of-distribution (OOD) generative models, since they primarily rely on model-specific artifacts and thus overfit to the models used for training. To address this limitation, we propose a novel representation, namely Semantic-Aware Reconstruction Error (SARE), that measures the semantic difference between an image and its caption-guided reconstruction. The key hypothesis behind SARE is that real images, whose captions often fail to fully capture their complex visual content, may undergo noticeable semantic shifts during the caption-guided reconstruction process. In contrast, fake images, which closely align with their captions, show minimal semantic changes. By quantifying these semantic shifts, SARE provides a robust and discriminative feature for detecting fake images across diverse generative models. Additionally, we introduce a fusion module that integrates SARE into the backbone detector via a cross-attention mechanism. Image features attend to semantic representations extracted from SARE, enabling the model to adaptively leverage semantic information. Experimental results demonstrate that the proposed method achieves strong generalization, outperforming existing baselines on benchmarks including GenImage and ForenSynths. We further validate the effectiveness of caption guidance through a detailed analysis of semantic shifts, confirming its ability to enhance detection robustness.

cs.CV↗

arXiv2025

Neutralization of IMU-Based GPS Spoofing Detection using external IMU sensor and feedback methodology

Autonomous Vehicles (AVs) refer to systems capable of perceiving their states and moving without human intervention. Among the factors required for autonomous decision-making in mobility, positional awareness of the vehicle itself is the most critical. Accordingly, extensive research has been conducted on defense mechanisms against GPS spoofing attacks, which threaten AVs by disrupting position recognition. Among these, detection methods based on internal IMU sensors are regarded as some of the most effective. In this paper, we propose a spoofing attack system designed to neutralize IMU sensor-based detection. First, we present an attack modeling approach for bypassing such detection. Then, based on EKF sensor fusion, we experimentally analyze both the impact of GPS spoofing values on the internal target system and how our proposed methodology reduces anomaly detection within the target system. To this end, this paper proposes an attack model that performs GPS spoofing by stealing internal dynamic state information using an external IMU sensor, and the experimental results demonstrate that attack values can be injected without being detected.

cs.CR↗

arXiv2025

CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs

Recently, inference-time reasoning strategies have further improved the accuracy of large language models (LLMs), but their effectiveness on smaller models remains unclear. Based on the observation that conventional approaches often fail to improve performance in this context, we propose \textbf{C}ycle-\textbf{C}onsistency in \textbf{Q}uestion \textbf{A}nswering (CCQA), a novel reasoning method that can be effectively applied to SLMs. Inspired by cycle consistency, CCQA generates a question from each reasoning path and answer, evaluates each by its similarity to the original question, and then selects the candidate solution with the highest similarity score as the final response. Since conventional SLMs struggle to generate accurate questions from their own reasoning paths and answers, we employ a lightweight Flan-T5 model specialized for question generation to support this process efficiently. From the experimental results, it is verified that CCQA consistently outperforms existing state-of-the-art (SOTA) methods across eight models on mathematical and commonsense reasoning benchmarks. Furthermore, our method establishes a new practical baseline for efficient reasoning in SLMs. Source code can be found at https://github.com/scai-research/ccqa_official.

cs.CL↗

arXiv2025

Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus

Location-based services often require users to share sensitive locational data, raising privacy concerns due to potential misuse or exploitation by untrusted servers. In response, we present VeLoPIR, a versatile location-based private information retrieval (PIR) system designed to preserve user privacy while enabling efficient and scalable query processing. VeLoPIR introduces three operational modes-interval validation, coordinate validation, and identifier matching-that support a broad range of real-world applications, including information and emergency alerts. To enhance performance, VeLoPIR incorporates multi-level algorithmic optimizations with parallel structures, achieving significant scalability across both CPU and GPU platforms. We also provide formal security and privacy proofs, confirming the system's robustness under standard cryptographic assumptions. Extensive experiments on real-world datasets demonstrate that VeLoPIR achieves up to 11.55 times speed-up over a prior baseline. The implementation of VeLoPIR is publicly available at https://github.com/PrivStatBool/VeLoPIR.

cs.CR↗

arXiv2025

Bidirectional Biometric Authentication Using Transciphering and (T)FHE

Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption (FHE) enables secure encrypted evaluation, but its deployment is hindered by large ciphertexts, high key overhead, and limited trust models. We propose the Bidirectional Transciphering Framework (BTF), combining FHE, transciphering, and a non-colluding trusted party to enable efficient and privacy-preserving biometric authentication. The key architectural innovation is the introduction of a trusted party that assists in evaluation and key management, along with a double encryption mechanism to preserve the FHE trust model, where client data remains private. BTF addresses three core deployment challenges: reducing the size of returned FHE ciphertexts, preventing clients from falsely reporting successful authentication, and enabling scalable, centralized FHE key management. We implement BTF using TFHE and the Trivium cipher, and evaluate it on iris-based biometric data. Our results show up to a 121$\times$ reduction in transmission size compared to standard FHE models, demonstrating practical scalability and deployment potential.

cs.CR↗

arXiv2025

GPS Spoofing Attacks on AI-based Navigation Systems with Obstacle Avoidance in UAV

Recently, approaches using Deep Reinforcement Learning (DRL) have been proposed to solve UAV navigation systems in complex and unknown environments. However, despite extensive research and attention, systematic studies on various security aspects have not yet been conducted. Therefore, in this paper, we conduct research on security vulnerabilities in DRL-based navigation systems, particularly focusing on GPS spoofing attacks against the system. Many recent basic DRL-based navigation systems fundamentally share an efficient structure. This paper presents an attack model that operates through GPS spoofing attacks briefly modeling the range of spoofing attack against EKF sensor fusion of PX4 autopilot, and combine this with the DRL-based system to design attack scenarios that are closer to reality. Finally, this paper experimentally demonstrated that attacks are possible both in the basic DRL system and in attack models combining the DRL system with PX4 autopilot system.

cs.CR↗

arXiv2025

FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning

Recently, fake audio detection has gained significant attention, as advancements in speech synthesis and voice conversion have increased the vulnerability of automatic speaker verification (ASV) systems to spoofing attacks. A key challenge in this task is generalizing models to detect unseen, out-of-distribution (OOD) attacks. Although existing approaches have shown promising results, they inherently suffer from overconfidence issues due to the usage of softmax for classification, which can produce unreliable predictions when encountering unpredictable spoofing attempts. To deal with this limitation, we propose a novel framework called fake audio detection with evidential learning (FADEL). By modeling class probabilities with a Dirichlet distribution, FADEL incorporates model uncertainty into its predictions, thereby leading to more robust performance in OOD scenarios. Experimental results on the ASVspoof2019 Logical Access (LA) and ASVspoof2021 LA datasets indicate that the proposed method significantly improves the performance of baseline models. Furthermore, we demonstrate the validity of uncertainty estimation by analyzing a strong correlation between average uncertainty and equal error rate (EER) across different spoofing algorithms.

eess.AS↗

arXiv2024

Towards Maximum Likelihood Training for Transducer-based Streaming Speech Recognition

Transducer neural networks have emerged as the mainstream approach for streaming automatic speech recognition (ASR), offering state-of-the-art performance in balancing accuracy and latency. In the conventional framework, streaming transducer models are trained to maximize the likelihood function based on non-streaming recursion rules. However, this approach leads to a mismatch between training and inference, resulting in the issue of deformed likelihood and consequently suboptimal ASR accuracy. We introduce a mathematical quantification of the gap between the actual likelihood and the deformed likelihood, namely forward variable causal compensation (FoCC). We also present its estimator, FoCCE, as a solution to estimate the exact likelihood. Through experiments on the LibriSpeech dataset, we show that FoCCE training improves the accuracy of the streaming transducers.

eess.AS↗

arXiv2024

Fast Private Location-based Information Retrieval Over the Torus

Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy when retrieving data from public clouds. Our system employs TFHE's expertise in non-polynomial evaluations, crucial for comparison operations. LocPIR showcases minimal client-server interaction, reduced memory overhead, and efficient throughput. Performance tests confirm its computational speed, making it a viable solution for practical scenarios, demonstrated via application to a COVID-19 alert model. Thus, LocPIR effectively addresses privacy concerns in location-based services, enabling secure data sharing from the public cloud.

cs.CR↗

arXiv2024

HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition

Pre-training with self-supervised models, such as Hidden-unit BERT (HuBERT) and wav2vec 2.0, has brought significant improvements in automatic speech recognition (ASR). However, these models usually require an expensive computational cost to achieve outstanding performance, slowing down the inference speed. To improve the model efficiency, we introduce an early exit scheme for ASR, namely HuBERT-EE, that allows the model to stop the inference dynamically. In HuBERT-EE, multiple early exit branches are added at the intermediate layers. When the intermediate prediction of the early exit branch is confident, the model stops the inference, and the corresponding result can be returned early. We investigate the proper early exiting criterion and fine-tuning strategy to effectively perform early exiting. Experimental results on the LibriSpeech show that HuBERT-EE can accelerate the inference of the HuBERT while simultaneously balancing the trade-off between the performance and the latency.

cs.CL↗

arXiv2024

Speed-up of Data Analysis with Kernel Trick in Encrypted Domain

Homomorphic encryption (HE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. However, efficiently processing high-dimensional data in HE, especially for machine learning and statistical (ML/STAT) algorithms, poses a challenge. In this paper, we present an effective acceleration method using the kernel method for HE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains. This technique, independent of underlying HE mechanisms and complementing existing optimizations, notably reduces costly HE multiplications, offering near constant time complexity relative to data dimension. Aimed at accessibility, this method is tailored for data scientists and developers with limited cryptography background, facilitating advanced data analysis in secure environments.

cs.CR↗

arXiv2023

Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models

Knowledge distillation (KD), best known as an effective method for model compression, aims at transferring the knowledge of a bigger network (teacher) to a much smaller network (student). Conventional KD methods usually employ the teacher model trained in a supervised manner, where output labels are treated only as targets. Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input. Since the Oracle Teacher learns a more accurate CTC alignment by referring to the target information, it can provide the student with more optimal guidance. One potential risk for the proposed approach is a trivial solution that the model's output directly copies the target input. Based on a many-to-one mapping property of the CTC algorithm, we present a training strategy that can effectively prevent the trivial solution and thus enables utilizing both source and target inputs for model training. Extensive experiments are conducted on two sequence learning tasks: speech recognition and scene text recognition. From the experimental results, we empirically show that the proposed model improves the students across these tasks while achieving a considerable speed-up in the teacher model's training time.

cs.LG↗

arXiv2023

MCR-Data2vec 2.0: Improving Self-supervised Speech Pre-training via Model-level Consistency Regularization

Self-supervised learning (SSL) has shown significant progress in speech processing tasks. However, despite the intrinsic randomness in the Transformer structure, such as dropout variants and layer-drop, improving the model-level consistency remains under-explored in the speech SSL literature. To address this, we propose a new pre-training method that uses consistency regularization to improve Data2vec 2.0, the recent state-of-the-art (SOTA) SSL model. Specifically, the proposed method involves sampling two different student sub-models within the Data2vec 2.0 framework, enabling two output variants derived from a single input without additional parameters. Subsequently, we regularize the outputs from the student sub-models to be consistent and require them to predict the representation of the teacher model. Our experimental results demonstrate that the proposed approach improves the SSL model's robustness and generalization ability, resulting in SOTA results on the SUPERB benchmark.

eess.AS↗

arXiv2023

EM-Network: Oracle Guided Self-distillation for Sequence Learning

We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning. In contrast to conventional methods, it is trained with oracle guidance, which is derived from the target sequence. Since the oracle guidance compactly represents the target-side context that can assist the sequence model in solving the task, the EM-Network achieves a better prediction compared to using only the source input. To allow the sequence model to inherit the promising capability of the EM-Network, we propose a new self-distillation strategy, where the original sequence model can benefit from the knowledge of the EM-Network in a one-stage manner. We conduct comprehensive experiments on two types of seq2seq models: connectionist temporal classification (CTC) for speech recognition and attention-based encoder-decoder (AED) for machine translation. Experimental results demonstrate that the EM-Network significantly advances the current state-of-the-art approaches, improving over the best prior work on speech recognition and establishing state-of-the-art performance on WMT'14 and IWSLT'14.

cs.LG↗

arXiv2023

Development of deep biological ages aware of morbidity and mortality based on unsupervised and semi-supervised deep learning approaches

Background: While deep learning technology, which has the capability of obtaining latent representations based on large-scale data, can be a potential solution for the discovery of a novel aging biomarker, existing deep learning methods for biological age estimation usually depend on chronological ages and lack of consideration of mortality and morbidity that are the most significant outcomes of aging. Methods: This paper proposes a novel deep learning model to learn latent representations of biological aging in regard to subjects' morbidity and mortality. The model utilizes health check-up data in addition to morbidity and mortality information to learn the complex relationships between aging and measured clinical attributes. Findings: The proposed model is evaluated on a large dataset of general populations compared with KDM and other learning-based models. Results demonstrate that biological ages obtained by the proposed model have superior discriminability of subjects' morbidity and mortality.

cs.LG↗