7 papers
Filtering Memorization from Parameter-Space in Diffusion Models
Yu Zhe, Yang Jiayan, Wei Junhao +2
Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles through lightweight parameter…
When Benchmarks Leak: Inference-Time Decontamination for LLMs
Jianzhe Chai, Yu Zhe, Jun Sakuma
Benchmark-based evaluation is the de facto standard for comparing large language models (LLMs). However, its reliability is increasingly threatened by test set contamination, where…
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy
Wei Junhao, Yu Zhe, Sakuma Jun
Model merging is a technique that combines multiple finetuned models into a single model without additional training, allowing a free-rider to cheaply inherit specialized capabilit…
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors
Chia-Yi Hsu, Yu-Lin Tsai, Yu Zhe +6
Task arithmetic in large-scale pre-trained models enables agile adaptation to diverse downstream tasks without extensive retraining. By leveraging task vectors (TVs), users can per…
Beyond Full Poisoning: Effective Availability Attacks with Partial Perturbation
Yu Zhe, Jun Sakuma
The widespread use of publicly available datasets for training machine learning models raises significant concerns about data misuse. Availability attacks have emerged as a means f…
Zero-shot domain adaptation based on dual-level mix and contrast
Yu Zhe, Jun Sakuma
Zero-shot domain adaptation (ZSDA) is a domain adaptation problem in the situation that labeled samples for a target task (task of interest) are only available from the source doma…