5 citations · 9 across the 8 of their papers we have counts for
8 papers
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks
Ziqiang Li, Yueqi Zeng, Pengfei Xia +3
With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common fo…
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm
Yi Wu, Ziqiang Li, Heliang Zheng +2
Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific i…
Real is not True: Backdoor Attacks Against Deepfake Detection
Hong Sun, Ziqiang Li, Lei Liu +1
The proliferation of malicious deepfake applications has ignited substantial public apprehension, casting a shadow of doubt upon the integrity of digital media. Despite the develop…
Two-stream joint matching method based on contrastive learning for few-shot action recognition
Long Deng, Ziqiang Li, Bingxin Zhou +3
Although few-shot action recognition based on metric learning paradigm has achieved significant success, it fails to address the following issues: (1) inadequate action relation mo…
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation
Ziqiang Li, Chaoyue Wang, Xue Rui +3
Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From…
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
Ziqiang Li, Pengfei Xia, Hong Sun +3
As the number of parameters in Deep Neural Networks (DNNs) scales, the thirst for training data also increases. To save costs, it has become common for users and enterprises to del…