Search Results for author: Huamin Feng

Found 6 papers, 4 papers with code

Alleviating Structural Distribution Shift in Graph Anomaly Detection

1 code implementation25 Jan 2024 Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes.

Binary Classification Graph Anomaly Detection

Rumor Detection with Self-supervised Learning on Texts and Social Graph

no code implementations19 Apr 2022 Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang

At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.

Self-Supervised Learning

Temporal RoI Align for Video Object Recognition

1 code implementation8 Sep 2021 Tao Gong, Kai Chen, Xinjiang Wang, Qi Chu, Feng Zhu, Dahua Lin, Nenghai Yu, Huamin Feng

In this work, considering the features of the same object instance are highly similar among frames in a video, a novel Temporal RoI Align operator is proposed to extract features from other frames feature maps for current frame proposals by utilizing feature similarity.

Instance Segmentation Object +5

Poison Ink: Robust and Invisible Backdoor Attack

1 code implementation5 Aug 2021 Jie Zhang, Dongdong Chen, Qidong Huang, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, Nenghai Yu

As the image structure can keep its semantic meaning during the data transformation, such trigger pattern is inherently robust to data transformations.

Backdoor Attack Data Poisoning

Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication

no code implementations9 Apr 2021 Xiquan Guan, Huamin Feng, Weiming Zhang, Hang Zhou, Jie Zhang, Nenghai Yu

Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift.

Model Compression

Deep Model Intellectual Property Protection via Deep Watermarking

1 code implementation8 Mar 2021 Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, Nenghai Yu

By jointly training the target model and watermark embedding, the extra barrier can even be absorbed into the target model.

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