Search Results for author:
Found 46 papers, 24 papers with code
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
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.
Image Manipulation Detection by Multi-View Multi-Scale Supervision
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.
Ranked #4 on
Image Manipulation Localization
on COVERAGE
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
As both clues are meant to be semantic-agnostic, the learned features are thus generalizable.
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances.
MDFEND: Multi-domain Fake News Detection
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.
Combating Online Misinformation Videos: Characterization, Detection, and Future Directions
With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem.
Mining Dual Emotion for Fake News Detection
Emotion plays an important role in detecting fake news online.
Zoom Out and Observe: News Environment Perception for Fake News Detection
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.
Deepfake Network Architecture Attribution
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images.
Memory-Guided Multi-View Multi-Domain Fake News Detection
In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
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.
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
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.
Dense Scale Network for Crowd Counting
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.
Progressive Domain Expansion Network for Single Domain Generalization
A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains.
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning
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.
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims
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.
Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
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.
Progressive Open Space Expansion for Open-Set Model Attribution
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.
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
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.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection
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.
Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling
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.
Understanding News Creation Intents: Frame, Dataset, and Method
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.
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
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.
Image Credibility Analysis with Effective Domain Transferred Deep Networks
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.
Tree-structured Kronecker Convolutional Network for Semantic Segmentation
Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.
Not All Words are Equal: Video-specific Information Loss for Video Captioning
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.
How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style
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.
Exploiting Multi-domain Visual Information for Fake News Detection
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.
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
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.
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views.
Combat Data Shift in Few-shot Learning with Knowledge Graph
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.
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model.
Learning to Disentangle GAN Fingerprint for Fake Image Attribution
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.
Quantifying Robustness to Adversarial Word Substitutions
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking
In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST.
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
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).
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space
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.
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
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.
Fingerprints of Generative Models in the Frequency Domain
It is verified in existing works that CNN-based generative models leave unique fingerprints on generated images.
SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection
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.
Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting
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.
Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow
The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities.
Adversarial Robust Memory-Based Continual Learner
Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed.
Topology-Preserving Adversarial Training
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
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.
