14 citations · 21 across the 16 of their papers we have counts for
17 papers
APT: Anchor-aligned Perturbations for Tamper Localization in Fully Regenerated Images
Suhyeon Ha, Woo Jae Kim, Joonsung Jeon +2
Proactive tamper localization embeds an imperceptible signal into an image prior to distribution, enabling pixel-level manipulation detection. Existing methods assume a spliced (SP…
EvBS: Event-guided Blur Synthesis for Domain-adaptive Motion Deblurring
Junsik Jung, Seokryun Choi, Yoonki Cho +3
Motion deblurring has achieved remarkable progress with deep learning, yet pre-trained deblurring models often suffer from performance degradation in real-world scenarios due to th…
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
Woo Jae Kim, Kyle Min, Suhyeon Ha +2
Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple perturbations but suffers from robustness trade-offs between different threats. T…
Radiometrically Consistent Gaussian Surfels for Inverse Rendering
Kyu Beom Han, Jaeyoon Kim, Woo Jae Kim +2
Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect ill…
No Caption, No Problem: Caption-Free Membership Inference via Model-Fitted Embeddings
Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2
Latent diffusion models have achieved remarkable success in high-fidelity text-to-image generation, but their tendency to memorize training data raises critical privacy and intelle…
Learning Event-guided Exposure-agnostic Video Frame Interpolation via Adaptive Feature Blending
Junsik Jung, Yoonki Cho, Woo Jae Kim +2
Exposure-agnostic video frame interpolation (VFI) is a challenging task that aims to recover sharp, high-frame-rate videos from blurry, low-frame-rate inputs captured under unknown…
AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fields
Woo Jae Kim, Kyu Beom Han, Yoonki Cho +4
As Neural Radiance Fields (NeRFs) have emerged as a powerful tool for 3D scene representation and novel view synthesis, protecting their intellectual property (IP) from unauthorize…
Pose-free 3D Gaussian splatting via shape-ray estimation
Youngju Na, Taeyeon Kim, Jumin Lee +3
While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-worl…
AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption
Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2
The outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversari…
Generalizable Person Re-identification via Balancing Alignment and Uniformity
Yoonki Cho, Jaeyoon Kim, Woo Jae Kim +2
Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straigh…
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Sets
Youngju Na, Woo Jae Kim, Kyu Beom Han +2
Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes. However, existin…
Deep Video Inpainting Guided by Audio-Visual Self-Supervision
Kyuyeon Kim, Junsik Jung, Woo Jae Kim +1
Humans can easily imagine a scene from auditory information based on their prior knowledge of audio-visual events. In this paper, we mimic this innate human ability in deep learnin…
Towards Content-based Pixel Retrieval in Revisited Oxford and Paris
Guoyuan An, Woo Jae Kim, Saelyne Yang +3
This paper introduces the first two pixel retrieval benchmarks. Pixel retrieval is segmented instance retrieval. Like semantic segmentation extends classification to the pixel leve…
Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising
Kyu Beom Han, Olivia G. Odenthal, Woo Jae Kim +1
Auxiliary features such as geometric buffers (G-buffers) and path descriptors (P-buffers) have been shown to significantly improve Monte Carlo (MC) denoising. However, recent appro…
Feature Separation and Recalibration for Adversarial Robustness
Woo Jae Kim, Yoonki Cho, Junsik Jung +1
Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deact…
Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks
Woo Jae Kim, Seunghoon Hong, Sung-Eui Yoon
Adversarial attacks with improved transferability - the ability of an adversarial example crafted on a known model to also fool unknown models - have recently received much attenti…
Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
Yoonki Cho, Woo Jae Kim, Seunghoon Hong +1
Unsupervised person re-identification (re-ID) aims at learning discriminative representations for person retrieval from unlabeled data. Recent techniques accomplish this task by us…