3 citations · 5 across the 8 of their papers we have counts for
10 papers
Can Native Safety Travel? Reusing Safety Pretraining Across Models via On-Policy Distillation
Bingqi Shang, Yang Zhang, Yiwei Chen +4
Safety pretraining has emerged as a promising paradigm for embedding safety deeply into a model during pretraining, yielding robustness to subsequent fine-tuning that conventional…
Forgetting to Forget: Attention Sink as A Gateway for Backdooring LLM Unlearning
Bingqi Shang, Yiwei Chen, Yihua Zhang +2
Large language model (LLM) unlearning is a key approach for removing undesired data, knowledge, or behaviors from pretrained models while retaining their general utility. Yet, with…
Whisper Smarter, not Harder: Adversarial Attack on Partial Suppression
Zheng Jie Wong, Bingquan Shen
Currently, Automatic Speech Recognition (ASR) models are deployed in an extensive range of applications. However, recent studies have demonstrated the possibility of adversarial at…
One Small Step with Fingerprints, One Giant Leap for De Novo Molecule Generation from Mass Spectra
Neng Kai Nigel Neo, Lim Jing, Ngoui Yong Zhau Preston +2
A common approach to the de novo molecular generation problem from mass spectra involves a two-stage pipeline: (1) encoding mass spectra into molecular fingerprints, followed by (2…
Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning
Yiwei Chen, Yuguang Yao, Yihua Zhang +3
Recent vision language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images. However, their susceptibility to gene…
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning
Zhifang Zhang, Shuo He, Haobo Wang +2
Multimodal contrastive learning models (e.g., CLIP) can learn high-quality representations from large-scale image-text datasets, while they exhibit significant vulnerabilities to b…
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency
Soumyadeep Pal, Yuguang Yao, Ren Wang +2
Modern machine learning (ML) systems demand substantial training data, often resorting to external sources. Nevertheless, this practice renders them vulnerable to backdoor poisonin…
From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion Models
Zhuoshi Pan, Yuguang Yao, Gaowen Liu +4
While state-of-the-art diffusion models (DMs) excel in image generation, concerns regarding their security persist. Earlier research highlighted DMs' vulnerability to data poisonin…
Tackling VQA with Pretrained Foundation Models without Further Training
Alvin De Jun Tan, Bingquan Shen
Large language models (LLMs) have achieved state-of-the-art results in many natural language processing tasks. They have also demonstrated ability to adapt well to different tasks…
A Novel real-time arrhythmia detection model using YOLOv8
Guang Jun Nicholas Ang, Aritejh Kr Goil, Henryk Chan +6
In a landscape characterized by heightened connectivity and mobility, coupled with a surge in cardiovascular ailments, the imperative to curtail healthcare expenses through remote…