51 citations · 139 across the 35 of their papers we have counts for
6 papers · 1 filter
BiDM: Pushing the Limit of Quantization for Diffusion Models
Xingyu Zheng, Xianglong Liu, Yichen Bian +5
Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities. However, the expensive computation and…
Behavior Backdoor for Deep Learning Models
Jiakai Wang, Pengfei Zhang, Renshuai Tao +5
The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence…
DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time
Jin Hu, Xianglong Liu, Jiakai Wang +5
Physical adversarial examples (PAEs) are regarded as whistle-blowers of real-world risks in deep-learning applications, thus worth further investigation. However, current PAE gener…
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
Yanni Xue, Haojie Hao, Jiakai Wang +5
While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer be…
Compromising Embodied Agents with Contextual Backdoor Attacks
Aishan Liu, Yuguang Zhou, Xianglong Liu +9
Large language models (LLMs) have transformed the development of embodied intelligence. By providing a few contextual demonstrations, developers can utilize the extensive internal…
BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models
Xingyu Zheng, Xianglong Liu, Haotong Qin +7
With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and effici…