1 citations · 1 across the 8 of their papers we have counts for
9 papers
Neural Audio Codec for Robust Audio Deepfake Detection
Jungwoo Kim, Joonyong Park, Junyoung Koh +1
Audio deepfake detectors are typically evaluated on uncompressed audio, although real-world audio often undergoes low-bitrate coding. In this work, we investigate how audio coding…
DRIFT: Derailing Denoising Trajectories of Flow-Matching VLAs with Adversarial Patch Attack
Hoseong Tae, Jong-Seok Lee
Flow-matching vision-language-action (VLA) models such as pi0 generate robot actions by integrating a learned denoising velocity field, and have been reported to resist adversarial…
Pooling-Based Context Modeling for Convolution-Free Deep Image Prior
Gihyun Kim, Jong-Seok Lee
Convolutional Neural Networks (CNNs) achieve strong denoising performance by exploiting spatial context from neighboring pixels. Deep Image Prior (DIP) leverages this property to r…
Dominant vs. Dominated: Concept-Level Generative Collapse in Diffusion Models
Hayeon Jeong, Jong-Seok Lee
Text-to-image diffusion models have attracted significant attention for their ability to generate diverse, high-fidelity images. However, in multi-concept generation, one concept t…
Exploring the Camera Bias of Person Re-identification
Myungseo Song, Jin-Woo Park, Jong-Seok Lee
We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largel…
Emotional EEG Classification using Upscaled Connectivity Matrices
Chae-Won Lee, Jong-Seok Lee
In recent studies of emotional EEG classification, connectivity matrices have been successfully employed as input to convolutional neural networks (CNNs), which can effectively con…
SC-Pro: Training-Free Framework for Defending Unsafe Image Synthesis Attack
Junha Park, Jaehui Hwang, Ian Ryu +3
With advances in diffusion models, image generation has shown significant performance improvements. This raises concerns about the potential abuse of image generation, such as the…
Impact of Regularization on Calibration and Robustness: from the Representation Space Perspective
Jonghyun Park, Juyeop Kim, Jong-Seok Lee
Recent studies have shown that regularization techniques using soft labels, e.g., label smoothing, Mixup, and CutMix, not only enhance image classification accuracy but also mitiga…
Adaptive Explicit Knowledge Transfer for Knowledge Distillation
Hyungkeun Park, Jong-Seok Lee
Logit-based knowledge distillation (KD) for classification is cost-efficient compared to feature-based KD but often subject to inferior performance. Recently, it was shown that the…