597 citations · 2.3k across the 106 of their papers we have counts for
16 papers · 1 filter
Are Fewer Labels Possible for Few-shot Learning?
Suichan Li, Dongdong Chen, Yinpeng Chen +4
Few-shot learning is challenging due to its very limited data and labels. Recent studies in big transfer (BiT) show that few-shot learning can greatly benefit from pretraining on l…
Identity-Driven DeepFake Detection
Xiaoyi Dong, Jianmin Bao, Dongdong Chen +5
DeepFake detection has so far been dominated by ``artifact-driven'' methods and the detection performance significantly degrades when either the type of image artifacts is unknown…
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation
Tianyi Wei, Dongdong Chen, Wenbo Zhou +4
Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produ…
Efficient Semantic Image Synthesis via Class-Adaptive Normalization
Zhentao Tan, Dongdong Chen, Qi Chu +6
Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis \cite{park2019semantic}, which modulates the normalized activatio…
Passport-aware Normalization for Deep Model Protection
Jie Zhang, Dongdong Chen, Jing Liao +3
Despite tremendous success in many application scenarios, deep learning faces serious intellectual property (IP) infringement threats. Considering the cost of designing and trainin…
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
Hang Zhou, Dongdong Chen, Jing Liao +6
Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samp…
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
Zhentao Tan, Menglei Chai, Dongdong Chen +5
Despite the recent success of face image generation with GANs, conditional hair editing remains challenging due to the under-explored complexity of its geometry and appearance. In…
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Xiaoyi Dong, Dongdong Chen, Jianmin Bao +5
Modern deep neural networks(DNNs) are vulnerable to adversarial samples. Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by on…
Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
Ruizhe Gu, Ping Wang, Mengce Zheng +2
Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. T…
An Enhanced Convolutional Neural Network in Side-Channel Attacks and Its Visualization
Minhui Jin, Mengce Zheng, Honggang Hu +1
In recent years, the convolutional neural networks (CNNs) have received a lot of interest in the side-channel community. The previous work has shown that CNNs have the potential of…
Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry
Zehua Ma, Weiming Zhang, Han Fang +3
As an efficient watermark attack method, geometric distortions destroy the synchronization between watermark encoder and decoder. And the local geometric distortion is a famous cha…
Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks
Ping Wang, Ping Chen, Zhimin Luo +4
Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for succe…
Rethinking Spatially-Adaptive Normalization
Zhentao Tan, Dongdong Chen, Qi Chu +5
Spatially-adaptive normalization is remarkably successful recently in conditional semantic image synthesis, which modulates the normalized activation with spatially-varying transfo…
Density-Aware Graph for Deep Semi-Supervised Visual Recognition
Suichan Li, Bin Liu, Dongdong Chen +3
Semi-supervised learning (SSL) has been extensively studied to improve the generalization ability of deep neural networks for visual recognition. To involve the unlabelled data, mo…
Cross-modality Person re-identification with Shared-Specific Feature Transfer
Yan Lu, Yue Wu, Bin Liu +4
Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. Existing works mainly focus on learning common representation…
Model Watermarking for Image Processing Networks
Jie Zhang, Dongdong Chen, Jing Liao +5
Deep learning has achieved tremendous success in numerous industrial applications. As training a good model often needs massive high-quality data and computation resources, the lea…