97 citations · 847 across the 68 of their papers we have counts for
15 papers · 1 filter
Invisible Backdoor Attack with Sample-Specific Triggers
Yuezun Li, Yiming Li, Baoyuan Wu +3
Recently, backdoor attacks pose a new security threat to the training process of deep neural networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the a…
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
Weihao Xia, Yujiu Yang, Jing-Hao Xue +1
In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: S…
Open-sourced Dataset Protection via Backdoor Watermarking
Yiming Li, Ziqi Zhang, Jiawang Bai +3
The rapid development of deep learning has benefited from the release of some high-quality open-sourced datasets (, ImageNet), which allows researchers to easily verify the e…
Pixel-wise Dense Detector for Image Inpainting
Ruisong Zhang, Weize Quan, Baoyuan Wu +2
Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of vi…
Dual ResGCN for Balanced Scene GraphGeneration
Jingyi Zhang, Yong Zhang, Baoyuan Wu +3
Visual scene graph generation is a challenging task. Previous works have achieved great progress, but most of them do not explicitly consider the class imbalance issue in scene gra…
Teacher-Student Competition for Unsupervised Domain Adaptation
Ruixin Xiao, Zhilei Liu, Baoyuan Wu
With the supervision from source domain only in class-level, existing unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant representations from a shared f…
Backdoor Attack against Speaker Verification
Tongqing Zhai, Yiming Li, Ziqi Zhang +3
Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount o…
SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning
Junbing Li, Changqing Zhang, Pengfei Zhu +3
Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels. Furthermore, modeling the rela…
Backdoor Learning: A Survey
Yiming Li, Yong Jiang, Zhifeng Li +1
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be mal…
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
Yan Feng, Baoyuan Wu, Yanbo Fan +3
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while…
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
Chengcheng Ma, Baoyuan Wu, Shibiao Xu +4
Adversarial examples have been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples, based on the assumption…
Rethinking the Trigger of Backdoor Attack
Yiming Li, Tongqing Zhai, Baoyuan Wu +3
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden bac…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning
Zhilei Liu, Yunpeng Wu, Le Li +2
Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the…
Controllable Descendant Face Synthesis
Yong Zhang, Le Li, Zhilei Liu +3
Kinship face synthesis is an interesting topic raised to answer questions like "what will your future children look like?". Published approaches to this topic are limited. Most of…