62 citations · 116 across the 20 of their papers we have counts for
20 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…
Efficient Trigger Word Insertion
Yueqi Zeng, Ziqiang Li, Pengfei Xia +2
With the boom in the natural language processing (NLP) field these years, backdoor attacks pose immense threats against deep neural network models. However, previous works hardly c…
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…
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
Ziqiang Li, Hong Sun, Pengfei Xia +4
Recent deep neural networks (DNNs) have came to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry…
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks
Ziqiang Li, Hong Sun, Pengfei Xia +6
Poisoning efficiency is crucial in poisoning-based backdoor attacks, as attackers aim to minimize the number of poisoning samples while maximizing attack efficacy. Recent studies h…
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
Yi Wu, Ziqiang Li, Chaoyue Wang +4
In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only…
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Ziqiang Li, Chaoyue Wang, Heliang Zheng +2
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since…
Data-Efficient Backdoor Attacks
Pengfei Xia, Ziqiang Li, Wei Zhang +1
Recent studies have proven that deep neural networks are vulnerable to backdoor attacks. Specifically, by mixing a small number of poisoned samples into the training set, the behav…
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Ziqiang Li, Beihao Xia, Jing Zhang +2
Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis. These successes of GANs rely on large scale datasets, requiring too much cost. With…
Enhancing Backdoor Attacks with Multi-Level MMD Regularization
Pengfei Xia, Hongjing Niu, Ziqiang Li +1
While Deep Neural Networks (DNNs) excel in many tasks, the huge training resources they require become an obstacle for practitioners to develop their own models. It has become comm…
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search
Pengfei Xia, Ziqiang Li, Bin Li
Despite achieving great success, Deep Neural Networks (DNNs) are vulnerable to adversarial examples. How to accurately evaluate the adversarial robustness of DNNs is critical for t…
Exploring The Effect of High-frequency Components in GANs Training
Ziqiang Li, Pengfei Xia, Xue Rui +1
Generative Adversarial Networks (GANs) have the ability to generate images that are visually indistinguishable from real images. However, recent studies have revealed that generate…
Understanding the Error in Evaluating Adversarial Robustness
Pengfei Xia, Ziqiang Li, Hongjing Niu +1
Deep neural networks are easily misled by adversarial examples. Although lots of defense methods are proposed, many of them are demonstrated to lose effectiveness when against prop…
A New Perspective on Stabilizing GANs training: Direct Adversarial Training
Ziqiang Li, Pengfei Xia, Rentuo Tao +2
Generative Adversarial Networks (GANs) are the most popular image generation models that have achieved remarkable progress on various computer vision tasks. However, training insta…
A Systematic Survey of Regularization and Normalization in GANs
Ziqiang Li, Muhammad Usman, Rentuo Tao +4
Generative Adversarial Networks (GANs) have been widely applied in different scenarios thanks to the development of deep neural networks. The original GAN was proposed based on the…
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation
Ziqiang Li, Rentuo Tao, Hongjing Niu +1
Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner pro…
DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention
Ziqiang Li, Rentuo Tao, Qianrun Wu +1
Automatic medical image segmentation has wide applications for disease diagnosing. However, it is much more challenging than natural optical image segmentation due to the high-reso…