29 citations · 62 across the 27 of their papers we have counts for
6 papers · 1 filter
How to Backdoor Diffusion Models?
Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho
Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via prog…
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
Lei Hsiung, Yung-Chen Tang, Pin-Yu Chen +1
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks. However, a lack of consid…
Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration
Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho
Neural network calibration is an essential task in deep learning to ensure consistency between the confidence of model prediction and the true correctness likelihood. In this paper…
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
Zhiyuan He, Yijun Yang, Pin-Yu Chen +2
Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs' vulnerability to Adversarial Examples (AE) hinders their deployments to safety-cri…
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…