28 citations · 47 across the 6 of their papers we have counts for
6 papers
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Yun-Yun Tsai, Ju-Chin Chao, Albert Wen +4
Training-time defenses, known as adversarial training, incur high training costs and do not generalize to unseen attacks. Test-time defenses solve these issues but most existing te…
Convolutional Visual Prompt for Robust Visual Perception
Yun-Yun Tsai, Chengzhi Mao, Junfeng Yang
Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale…
CARBEN: Composite Adversarial Robustness Benchmark
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen +1
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, mu…
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen +1
Model robustness against adversarial examples of single perturbation type such as the -norm has been widely studied, yet its generalization to more realistic scenarios in…
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Chao-Han Huck Yang, Yun-Yun Tsai, Pin-Yu Chen
Learning to classify time series with limited data is a practical yet challenging problem. Current methods are primarily based on hand-designed feature extraction rules or domain-s…
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho
Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data. Motivated by the techniques from adversarial machine learning (ML) that…