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Weiwei Jiang

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5 papers

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EAAI Journal 2026 Journal Article

Multi-granularity alignment and cross-modal reasoning for fake news video explanation

Chao Cheng
Weiwei Jiang

Fake news video explanation generation aims to provide accurate and insightful explanations through in-depth analysis of news video content. However, existing methods typically align video context with overall descriptions and generate explanations via multi-modal fusion, often neglecting the rich details of key semantic elements such as nouns and verbs. To address this limitation, this paper proposes a unified Artificial Intelligence (AI) framework named Multi-Granularity Alignment and Reasoning (MGAR). MGAR not only focuses on the semantic alignment of overall descriptions but also delves into the semantic elements in language, particularly nouns and verbs, and aligns them with frame-level and motion-level features of fake news videos for multi-granularity reasoning. Additionally, we design a unified residual-structured multi-granularity language module that employs a context exchange mechanism (e. g. , word-level and sentence-level) to adapt to semantic understanding at different granularity. Extensive experiments on the FakeVE dataset demonstrate the superiority of MGAR, achieving improvements of +10. 1% BLEU-1 and +11. 1% ROUGE-L over state-of-the-art baselines, showcasing the potential of AI applications in combating false information.

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IS Journal 2025 Journal Article

AI-Based Hate Speech Detection System Using Video URLs for Effective Content Moderation

Zohaib Ahmad Khan
Yuanqing Xia
Fiza Khaliq
Weiwei Jiang
Muhammad Shahid Anwar

Countering online hate speech is essential for creating a safer digital space where positive interactions can thrive. As central hubs of global communication, platforms like social media platforms require effective moderation through explainable and affective computing approaches. This study introduces a novel artificial intelligence-driven system for detecting misogynstic discourse. We collected 11, 245 YouTube video uniform resource locators using specific keywords, then extracted audio to create Urdu transcripts and transliterated them into Roman Urdu, resulting in two distinct datasets. Various feature sets were explored using classic machine learning and deep learning algorithms. The results showed that classical models achieved 0. 90 accuracy on the Urdu dataset, while deep learning models reached 0. 96 accuracy on Roman Urdu. The corpus is publicly available to promote transparency and further research. Comparative evaluations against existing English hate speech dataset demonstrate the effectiveness of the proposed approach. This work lays the foundation for more ethical and transparent content moderation systems.

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NeurIPS Conference 2025 Conference Paper

Is Artificial Intelligence Generated Image Detection a Solved Problem?

Ziqiang Li
Jiazhen Yan
Ziwen He
Kai Zeng
Weiwei Jiang
Lizhi Xiong
Zhangjie Fu

The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinformation, deepfakes, and copyright infringement. Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time pre-processing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of pre-processing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI detection.

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TAAS Journal 2025 Journal Article

Satellite Edge Computing for Mobile Multimedia Communications: A Multi-agent Federated Reinforcement Learning Approach

Weiwei Jiang
Yafeng Zhan
Xin Fang

The rapid expansion of satellite mega-constellations has highlighted the potential of satellite edge computing as a promising solution for mobile multimedia communications. While reinforcement learning has been explored in satellite communication systems, significant challenges remain, including high latency and limited resources. This study addresses these challenges by focusing on the joint optimization of communication, computing, and caching resources in satellite edge computing to support mobile multimedia applications. A mixed-integer nonlinear programming (MINLP) problem is formulated with the objective of minimizing the total delay experienced by mobile users, subject to multidimensional resource capacity constraints, which are NP-hard and computationally intractable to solve in polynomial time. To address this complexity, we propose a multi-agent federated reinforcement learning (MAFRL) approach as an efficient solution. In this framework, each satellite operates as an autonomous learning agent equipped with an actor-critic network structure. The proposed MAFRL method demonstrates superior performance, achieving lower delays compared to all baseline approaches. It effectively optimizes delay-sensitive mobile multimedia communications by minimizing total delay and improving task-offloading ratios. To the best of the authors’ knowledge, this study is the first to introduce an MAFRL-based approach for resource allocation in satellite edge computing, marking a significant contribution to the field.

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EAAI Journal 2023 Journal Article

Weight prediction and recognition of latent subject terms based on the fusion of explicit & implicit information about keyword

Shuqing Li
Mingfeng Jiang
Weiwei Jiang
Jingwang Huang
Hu Zhang
Zhiwang Zhang

Weight prediction and recognition of subject term in documents is widely used in application fields such as literature recommendation and keyword retrieval. Unlike traditional methods which only focus on searching for subject terms from existing keywords in documents, this paper proposes a recognition method of latent subject term outside documents based on a one-class collaborative filtering algorithm that fuses explicit and implicit information. A matrix factorization model based on the analysis of document activity and subject term popularity is constructed to measure the correlation probability between documents and subject terms that do not appear in the current document. After these subject terms being divided into Latent Subject Terms (LST) and Irrelevant Subject Terms (IST), two methods for predicting the weight of these types of subject terms is introduced, which include Hybrid Filling with Preference Coefficients (HFPC), and Zero Filling. In order to verify the effectiveness of subject term recognition, many collaborative filtering recommendation algorithms are conducted with the filled document keyword matrix. On the basis of not changing these algorithms, MAE and FCP can be improved by 28. 01% and 22. 79% at most, while P@N and NDCG@N can be improved by 22. 37% and 27. 06%, respectively.

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