AI Tools Can Generate Misculture Visuals! Detecting Prompts Generating Misculture Visuals For Prevention (2025)

Abstract: Advanced AI models that generate realistic images from text prompts offer new creative possibilities but also risk producing culturally insensitive or offensive content. To address this issue, we introduce a novel dataset designed to classify text prompts that could lead to the generation of harmful images misrepresenting different cultures and communities. By training machine learning models on this dataset, we aim to automatically identify and filter out harmful prompts before image generation, balancing cultural sensitivity with creative freedom. Benchmarking with state-ofthe-art language models, our baseline models achieved an accuracy of 73.34%.

## introduction the advent of ai image generation tools, fueled by advanced machine learning models, has ushered in a powerful new technology that promises to revolutionize various industries @xcite @xcite . these cutting-edge tools hold immense potential benefits, particularly for content creators, marketers, and professionals who heavily rely on visuals to convey their messages. by harnessing the capabilities of ai, these tools offer an unprecedented level of efficiency, allowing users to generate high-quality, visually captivating images on demand with minimal effort @xcite . this remarkable feat not only eases the workload but also accelerates creative workflows, enabling professionals to keep pace with the ever-increasing demand for visual content in our digital age @xcite @xcite . however, beyond mere time and effort savings, ai image generation unlocks a realm of creative possibilities that was once unimaginable. by simply providing textual prompts, users can now gen-erate a vast array of unique and compelling visuals, pushing the boundaries of what was previously thought possible. this technology empowers creators to transcend the limitations of traditional image creation methods, fostering innovation and enabling the exploration of uncharted creative territories. ai image generation holds great promise but poses significant risks when it produces images that misrepresent or appropriate cultural elements harmfully. these issues often stem from biases in training data or the ai's inability to grasp cultural nuances, leading to harmful stereotypes and distorted representations. preventing such unintended consequences is crucial as ai tools become more widespread. responsible development requires understanding these risks and adopting a multifaceted approach to mitigate them. this includes technological solutions to address biases, enhancing cultural sensitivity, and establishing ethical guidelines that prioritize diversity, inclusivity, and respect for cultural heritage. misculture prompts (mp) are inputs that lead to images inaccurately depicting a culture, perpetuating harmful stereotypes or offensive representations. in contrast, non-misculture prompts (nmp) are carefully crafted to avoid such biases, ensuring generated images accurately and respectfully portray cultural elements without misrepresentation or offense. motivation: ai text to image models has several applications and one of the interesting applications in the domain of ai. there is potential risk of misusing these models for mispresenting the culture through images as shown in fig. 1 . and fig. 2 . preventing the ai models to generate such images makes the models more safer. but how can we stop ai models generating misculture images? there is an option to make changes in the internal working but that may effect the image generation quality. so, doing changes internally is not a good option. then how can we do it? we can train a classifier that can classify the prompts that generate the misculture images. the main contributions of this paper are: ## related work previous studies have explored the spread of misinformation through large language models (llms), but none have focused on cultural misrepresentations. for instance, pan et al. (2023) examined how llms might generate false information and proposed mitigation techniques, yet did not address cultural or societal aspects. similarly, wang et al. (2024) developed methods to mitigate llm misuse in generating problematic content like hate speech, without tackling cultural or religious misinformation. other works highlighted security risks in llm outputs @xcite , mitigated gossip about celebrities @xcite , and detected llm-generated essays to prevent educational misuse @xcite , but again did not focus on cultural misrepresentations. existing research on the misuse of ai has examined various forms of misuse, such as biological applications and educational contexts. however, the potential misuse of ai tools to misrepresent cultures has not yet been explored. ## experimental results and discussion in terms of analysis, it is evident that the few shot setting generally yields better performance across most models compared to the zero shot setting. notably, llama 2 demonstrates a significant improvement when trained in the few shot context, moving from 60.15% to 70.28% accuracy. similarly, gpt-3.5 shows substantial gains in few shot learning, indicating the importance of providing models with some prior examples to improve prompt detection. the relatively lower performance of models like gemini in the zero shot setting highlights the challenge of generalizing without prior task-specific information. overall, the results underline the effectiveness of advanced models like gpt-3.5 and roberta, particularly when they can leverage few shot learning to enhance their detection capabilities. ## conclusion in this paper, we have presented a novel dataset and proposed a practical application to address the crucial issue of cultural misrepresentation in aigenerated visuals. as ai image generation tools become increasingly advanced and widespread, it is imperative to mitigate the risk of generat-ing visuals that misrepresent or perpetuate harmful stereotypes about different cultures. our proposed application aims to be seamlessly integrated into existing ai image generation tools, providing guidance and safeguards during the image generation process. by leveraging the specialized dataset curated for this research, the application can identify and correct potential misrepresentations, ensuring that the generated visuals accurately and sensitively depict cultural elements. through our experiments, we have achieved an accuracy of 73.34% in correctly representing cultural elements in the generated images.