All Changes May Have Invariant Principles: Improving Ever-Shifting Harmful Meme Detection via Design Concept Reproduction

• URL: http://arxiv.org/abs/2601.04567v1
• Date: Thu, 08 Jan 2026 03:49:49 GMT
• Title: All Changes May Have Invariant Principles: Improving Ever-Shifting Harmful Meme Detection via Design Concept Reproduction
• Authors: Ziyou Jiang, Mingyang Li, Junjie Wang, Yuekai Huang, Jie Huang, Zhiyuan Chang, Zhaoyang Li, Qing Wang,
• Abstract summary: We propose RepMD, an ever-shifting harmful meme detection method based on the design concept reproduction.<n>RepMD achieves the highest accuracy with 81.1% and has slight accuracy decreases when generalized to type-shifting and temporal-evolving memes.
• Score: 23.203924634469104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Harmful memes are ever-shifting in the Internet communities, which are difficult to analyze due to their type-shifting and temporal-evolving nature. Although these memes are shifting, we find that different memes may share invariant principles, i.e., the underlying design concept of malicious users, which can help us analyze why these memes are harmful. In this paper, we propose RepMD, an ever-shifting harmful meme detection method based on the design concept reproduction. We first refer to the attack tree to define the Design Concept Graph (DCG), which describes steps that people may take to design a harmful meme. Then, we derive the DCG from historical memes with design step reproduction and graph pruning. Finally, we use DCG to guide the Multimodal Large Language Model (MLLM) to detect harmful memes. The evaluation results show that RepMD achieves the highest accuracy with 81.1% and has slight accuracy decreases when generalized to type-shifting and temporal-evolving memes. Human evaluation shows that RepMD can improve the efficiency of human discovery on harmful memes, with 15$\sim$30 seconds per meme.

Related papers

• MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models [50.2355423914562]
  We introduce MemeReaCon, a novel benchmark designed to evaluate how Large Vision Language Models (LVLMs) understand memes in their original context.<n>We collected memes from five different Reddit communities, keeping each meme's image, the post text, and user comments together.<n>Our tests with leading LVLMs show a clear weakness: models either fail to interpret critical information in the contexts, or overly focus on visual details while overlooking communicative purpose.
  arXiv  Detail & Related papers  (2025-05-23T03:27:23Z)
• Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection [49.122777764853055]
  We explore the potential of Large Multimodal Models (LMMs) for hateful meme detection.<n>We propose Evolver, which incorporates LMMs via Chain-of-Evolution (CoE) Prompting.<n>Evolver simulates the evolving and expressing process of memes and reasons through LMMs in a step-by-step manner.
  arXiv  Detail & Related papers  (2024-07-30T17:51:44Z)
• XMeCap: Meme Caption Generation with Sub-Image Adaptability [53.2509590113364]
  Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines.<n>We introduce the textscXMeCap framework, which adopts supervised fine-tuning and reinforcement learning.<n>textscXMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 6.75% and 8.56%, respectively.
  arXiv  Detail & Related papers  (2024-07-24T10:51:46Z)
• What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation [0.9217021281095907]
  Multimodal Internet Memes are now a ubiquitous fixture in online discourse. Memetics are the process by which memes are imitated and transformed into symbols. We develop a meme identification protocol which distinguishes meme from non-memetic content by recognising the memetics within it.
  arXiv  Detail & Related papers  (2024-07-16T15:48:36Z)
• Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations [48.82168723932981]
  We introduce em MultiBully-Ex, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. A Contrastive Language-Image Pretraining (CLIP) approach has been proposed for visual and textual explanation of a meme.
  arXiv  Detail & Related papers  (2024-01-18T11:24:30Z)
• On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning [18.794226796466962]
  We study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes.
  arXiv  Detail & Related papers  (2022-12-13T13:38:04Z)
• Detecting Harmful Memes and Their Targets [27.25262711136056]
  We present HarMeme, the first benchmark dataset, containing 3,544 memes related to COVID-19. In the first stage, we labeled a meme as very harmful, partially harmful, or harmless; in the second stage, we further annotated the type of target(s) that each harmful meme points to. The evaluation results using ten unimodal and multimodal models highlight the importance of using multimodal signals for both tasks.
  arXiv  Detail & Related papers  (2021-09-24T17:11:42Z)
• MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets [28.877314859737197]
  We aim to solve two novel tasks: detecting harmful memes and identifying the social entities they target. In particular, we aim to solve two novel tasks: detecting harmful memes and identifying the social entities they target. We propose MOMENTA, a novel multimodal (text + image) deep neural model, which uses global and local perspectives to detect harmful memes.
  arXiv  Detail & Related papers  (2021-09-11T04:29:32Z)
• Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset [47.65948529524281]
  We collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild differ in two key aspects: 1) Captions must be extracted via OCR, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background.
  arXiv  Detail & Related papers  (2021-07-09T09:04:05Z)
• Entropy and complexity unveil the landscape of memes evolution [105.59074436693487]
  We study the evolution of 2 million visual memes from Reddit over ten years, from 2011 to 2020. We find support for the hypothesis that memes are part of an emerging form of internet metalanguage.
  arXiv  Detail & Related papers  (2021-05-26T07:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.