Search Results for author: Juan Cao

Found 46 papers, 24 papers with code

U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation

1 code implementation29 Mar 2024 You Wu, Kean Liu, Xiaoyue Mi, Fan Tang, Juan Cao, Jintao Li

Extensive experiments on various kinds of visual attributes with SOTA personalization methods show the ability of the proposed method to mimic target visual appearance in novel contexts, thus improving the controllability and flexibility of personalization.

Attribute Disentanglement +1

Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization

1 code implementation28 Mar 2024 Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang, Oliver Deussen, WeiMing Dong, Jintao Li, Tong-Yee Lee

Based on the adapters broken apart for separate training content and style, we then make the entity parameter space by reconstructing the content and style PLPs matrices, followed by fine-tuning the combined adapter to generate the target object with the desired appearance.

Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework

1 code implementation25 Mar 2024 Ziyao Huang, Fan Tang, Yong Zhang, Xiaodong Cun, Juan Cao, Jintao Li, Tong-Yee Lee

We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances.


Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling

1 code implementation14 Feb 2024 Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, Danding Wang

With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution.

Text Detection

Understanding News Creation Intents: Frame, Dataset, and Method

1 code implementation27 Dec 2023 Zhengjia Wang, Danding Wang, Qiang Sheng, Juan Cao, Silong Su, Yifan Sun, Beizhe Hu, Siyuan Ma

As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest.


Adversarial Robust Memory-Based Continual Learner

no code implementations29 Nov 2023 Xiaoyue Mi, Fan Tang, Zonghan Yang, Danding Wang, Juan Cao, Peng Li, Yang Liu

Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed.

Adversarial Robustness Continual Learning

Topology-Preserving Adversarial Training

no code implementations29 Nov 2023 Xiaoyue Mi, Fan Tang, Yepeng Weng, Danding Wang, Juan Cao, Sheng Tang, Peng Li, Yang Liu

Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i. e., accuracy on natural samples has reduced significantly.

Rethinking Image Editing Detection in the Era of Generative AI Revolution

no code implementations29 Nov 2023 Zhihao Sun, Haipeng Fang, Xinying Zhao, Danding Wang, Juan Cao

However, the lack of comprehensive dataset containing images edited with abundant and advanced generative regional editing methods poses a substantial obstacle to the advancement of corresponding detection methods.

Image Classification Image Manipulation +1

Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

no code implementations20 Oct 2023 Haipeng Fang, Zhihao Sun, Ziyao Huang, Fan Tang, Juan Cao, Sheng Tang

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities.

Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting

no code implementations16 Oct 2023 Qiong Nan, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Guang Yang, Jintao Li, Kai Shu

To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e. g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts.

Fake News Detection

Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

1 code implementation21 Sep 2023 Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, Peng Qi

To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales.

Fake News Detection

Fingerprints of Generative Models in the Frequency Domain

no code implementations29 Jul 2023 Tianyun Yang, Juan Cao, Danding Wang, Chang Xu

It is verified in existing works that CNN-based generative models leave unique fingerprints on generated images.

Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection

1 code implementation26 Jun 2023 Beizhe Hu, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Zhengjia Wang, Zhiwei Jin

In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data.

Fake News Detection

Progressive Open Space Expansion for Open-Set Model Attribution

1 code implementation CVPR 2023 Tianyun Yang, Danding Wang, Fan Tang, Xinying Zhao, Juan Cao, Sheng Tang

In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones.

Attribute Open Set Learning

Combating Online Misinformation Videos: Characterization, Detection, and Future Directions

2 code implementations7 Feb 2023 Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, Jintao Li

With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem.

Misinformation Recommendation Systems +1

SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection

no code implementations ICCV 2023 Zhihao Sun, Haoran Jiang, Danding Wang, Xirong Li, Juan Cao

Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features.

Image Manipulation Image Manipulation Detection

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

no code implementations COLING 2022 Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li

To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains.

Fake News Detection Language Modelling +2

Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space

no code implementations1 Sep 2022 Guang Yang, Wu Liu, Xinchen Liu, Xiaoyan Gu, Juan Cao, Jintao Li

To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos.

DeepFake Detection Face Swapping

Memory-Guided Multi-View Multi-Domain Fake News Detection

1 code implementation26 Jun 2022 Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, Fuzhen Zhuang

In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.

Fake News Detection

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.

Attribute DeepFake Detection +2

A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking

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

In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST.

Dialogue State Tracking Language Modelling

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 Sentence

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 Open-Ended Question Answering

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

3 code implementations31 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.

Marketing Meta-Learning +1

Image Manipulation Detection by Multi-View Multi-Scale Supervision

2 code implementations 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 +3

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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