Search Results for author: Juan Cao

Found 27 papers, 13 papers with code

Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo

1 code implementation6 May 2022 Qiang Sheng, Juan Cao, H. Russell Bernard, Kai Shu, Jintao Li, Huan Liu

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection

1 code implementation20 Apr 2022 Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, Fuzhen Zhuang

In this paper, we propose an entity debiasing framework (\textbf{ENDEF}) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective.

Fake News Detection

A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement

no code implementations21 Mar 2022 Yuting Yang, Pei Huang, Juan Cao, Jintao Li, Yun Lin, Jin Song Dong, Feifei Ma, Jian Zhang

Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).

Adversarial Attack

Zoom Out and Observe: News Environment Perception for Fake News Detection

1 code implementation ACL 2022 Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang, Yongchun Zhu

To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies.

Fake News Detection Misinformation

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

1 code implementation17 Mar 2022 Guang Yang, Juan Cao, Qiang Sheng, Peng Qi, Xirong Li, Jintao Li

However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.

Deepfake Network Architecture Attribution

1 code implementation28 Feb 2022 Tianyun Yang, Ziyao Huang, Juan Cao, Lei LI, Xirong Li

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images.

DeepFake Detection Fake Image Attribution

Prompt Learning for Few-Shot Dialogue State Tracking

no code implementations15 Jan 2022 Yuting Yang, Wenqiang Lei, Pei Huang, Juan Cao, Jintao Li, Tat-Seng Chua

Collecting dialogue state labels, slots and values, for learning dialogue state tracking (DST) models can be costly, especially with the wide application of dialogue systems in new-rising domains.

Dialogue State Tracking

Quantifying Robustness to Adversarial Word Substitutions

no code implementations11 Jan 2022 Yuting Yang, Pei Huang, Feifei Ma, Juan Cao, Meishan Zhang, Jian Zhang, Jintao Li

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.

MDFEND: Multi-domain Fake News Detection

1 code implementation4 Jan 2022 Qiong Nan, Juan Cao, Yongchun Zhu, Yanyan Wang, Jintao Li

In this paper, we first design a benchmark of fake news dataset for MFND with domain label annotated, namely Weibo21, which consists of 4, 488 fake news and 4, 640 real news from 9 different domains.

Fake News Detection

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

1 code implementation ACL 2021 Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, Lei Zhong

By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns.

Fact Checking

MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection

no code implementations16 Dec 2021 Chengbo Dong, Xinru Chen, Ruohan Hu, Juan Cao, Xirong Li

The key research question for image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images.

Image Manipulation Image Manipulation Detection +1

Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning

1 code implementation23 Sep 2021 Qiang Sheng, Xueyao Zhang, Juan Cao, Lei Zhong

To this end, we build a Preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern- and fact-based models for joint detection.

Fake News Detection

Learning to Disentangle GAN Fingerprint for Fake Image Attribution

no code implementations16 Jun 2021 Tianyun Yang, Juan Cao, Qiang Sheng, Lei LI, Jiaqi Ji, Xirong Li, Sheng Tang

Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution.

Fake Image Attribution

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

1 code implementation31 May 2021 Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin, Juan Cao

Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.

Meta-Learning Recommendation Systems

Image Manipulation Detection by Multi-View Multi-Scale Supervision

1 code implementation ICCV 2021 Xinru Chen, Chengbo Dong, Jiaqi Ji, Juan Cao, Xirong Li

The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images.

Image Manipulation Image Manipulation Detection +1

Combat Data Shift in Few-shot Learning with Knowledge Graph

no code implementations27 Jan 2021 Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi, Juan Cao, Qing He

However, in real-world applications, few-shot learning paradigm often suffers from data shift, i. e., samples in different tasks, even in the same task, could be drawn from various data distributions.

Few-Shot Learning

Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection

no code implementations ACL 2020 Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, Ziang Wang

Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views.

Exploiting Multi-domain Visual Information for Fake News Detection

no code implementations13 Aug 2019 Peng Qi, Juan Cao, Tianyun Yang, Junbo Guo, Jintao Li

In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively.

Fake News Detection

Dense Scale Network for Crowd Counting

1 code implementation24 Jun 2019 Feng Dai, Hao liu, Yike Ma, Juan Cao, Qiang Zhao, Yongdong Zhang

The key component of our network is the dense dilated convolution block, in which each dilation layer is densely connected with the others to preserve information from continuously varied scales.

Crowd Counting

How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style

no code implementations2 Feb 2019 Yuting Yang, Juan Cao, Mingyan Lu, Jintao Li, Chia-Wen Lin

SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to.

Not All Words are Equal: Video-specific Information Loss for Video Captioning

no code implementations1 Jan 2019 Jiarong Dong, Ke Gao, Xiaokai Chen, Junbo Guo, Juan Cao, Yongdong Zhang

To address this issue, we propose a novel learning strategy called Information Loss, which focuses on the relationship between the video-specific visual content and corresponding representative words.

Video Captioning

Tree-structured Kronecker Convolutional Network for Semantic Segmentation

no code implementations12 Dec 2018 Tianyi Wu, Sheng Tang, Rui Zhang, Juan Cao, Jintao Li

Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.

Semantic Segmentation

Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs

no code implementations Mountain View, CA, USA 2017 Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang

In this paper, we propose a novel Recurrent Neural Network with an at- tention mechanism (att-RNN) to fuse multimodal features for e ective rumor detection.

Image Credibility Analysis with Effective Domain Transferred Deep Networks

no code implementations16 Nov 2016 Zhiwei Jin, Juan Cao, Jiebo Luo, Yongdong Zhang

In order to overcome the scarcity of training samples of fake images, we first construct a large-scale auxiliary dataset indirectly related to this task.

Image Classification Transfer Learning

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