20 citations · 47 across the 6 of their papers we have counts for
10 papers
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
Yihua Zhang, Chongyu Fan, Yimeng Zhang +8
The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. Howev…
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
Lijie Fan, Sijia Liu, Pin-Yu Chen +2
Contrastive learning (CL) can learn generalizable feature representations and achieve the state-of-the-art performance of downstream tasks by finetuning a linear classifier on top…
Generating Adversarial Computer Programs using Optimized Obfuscations
Shashank Srikant, Sijia Liu, Tamara Mitrovska +4
Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed. These models have demonstrated success in applicati…
Fast Training of Provably Robust Neural Networks by SingleProp
Akhilan Boopathy, Tsui-Wei Weng, Sijia Liu +3
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. However, these techniques can be computationally cos…
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
Ren Wang, Gaoyuan Zhang, Sijia Liu +3
When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoni…
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura +3
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications. It is used for solving optimizati…