5 papers
Compression Asymmetry and Trajectory Binding in Noise-Anchored Diffusion Inversion
Yongseong Park, Joeun Kim, HoEun Kim +1
Real-image diffusion inversion is governed by a tight quality-cost trade-off, with costs incurred in computation, storage, or per-image optimization. We study this trade-off throug…
CORE-BREW: LLR-Based Soft Decoding for Robust Multi-Bit LLM Watermarking
Joeun Kim, HoEun Kim, Young-Sik Kim
Reliable provenance for LLM outputs requires multi-bit watermarks that remain robust under editing while maintaining strict false-positive control. Existing ECC-based LLM watermark…
Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking
Joeun Kim, HoEun Kim, Dongsup Jin +1
Recent multi-bit watermarking methods for large language models (LLMs) prioritize capacity over reliability, often conflating decoding with detection. Our analysis reveals that exi…
Mutual Information Minimization for Side-Channel Attack Resistance via Optimal Noise Injection
Jiheon Woo, Donggyun Ryu, Daewon Seo +4
Side-channel attacks (SCAs) pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electroma…
CGF-Softmax: A Cumulant-Based Softmax Reformulation for Efficient Inference under Homomorphic Encryption
Hanjun Park, Byeongseo Min, Jiheon Woo +5
Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. However, evaluating softmax, a core com…