App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Hateful meme detection based on semantic enhancement and image-guided adaptive fusion

Zhong Han
Feng Hao
College of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0161

Abstract

Hateful memes convey implicit aggressive, sarcastic, and discriminatory meanings through coordinated image-text expressions, which poses significant challenges for multimodal harmful meme detection. Existing methods leverage vision-language pre-training models or large language models to generate auxiliary text for enhanced meme understanding. However, three problems remain in these methods: mixed text evidence, insufficient independent discriminative capability, and lack of conditional adaptability in cross-modal fusion. To address these issues, a method was proposed based on semantic enhancement and image-guided adaptive fusion, named MED-ICAF (Multi-stream Evidence Decomposition with Image-Conditioned Aspect Fusion) . The method adopted multi-dimensional semantic decomposition encoding to construct multiple independent text evidence streams, achieving semantic enhancement. It also employed a reasoning-judge supervision module to enhance the independent discriminative capability of text evidence. Furthermore, the method adopted an image-guided adaptive fusion mechanism to dynamically select key aspect evidence and achieve deep image-text collaboration. Experimental results showed that the method achieved F1 scores of 86.49% and 75.90% on the Harmeme and FHM datasets, respectively, which verifies its effectiveness. These results demonstrated the effectiveness and advancement of semantic enhancement and adaptive fusion strategies. The proposed method exhibited significant advantages over existing approaches in cross-modal semantic alignment and fine-grained discrimination, providing a novel solution for hateful meme detection.

Foundation Support

高等学校学科创新引智基地项目(B20087)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0161
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-21] Accepted Paper

Cite This Article

钟寒, 冯浩. 基于语义增强和图像引导自适应融合的仇恨模因检测方法 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0161. (Zhong Han, Feng Hao. Hateful meme detection based on semantic enhancement and image-guided adaptive fusion [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0161. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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