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Found 27 papers, 13 papers with code
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.
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.
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).
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.
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.
Deepfake Network Architecture Attribution
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images.
Prompt Learning for Few-Shot Dialogue State Tracking
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.
Quantifying Robustness to Adversarial Word Substitutions
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
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.
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.
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
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.
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.
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.
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.
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.
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.
Progressive Domain Expansion Network for Single Domain Generalization
A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains.
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.
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.
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.
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.
Mining Dual Emotion for Fake News Detection
Emotion plays an important role in detecting fake news online.
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.
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.
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.
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.
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.
