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Relevant bibliographies by topics / AI image generators

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Academic literature on the topic 'AI image generators'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "AI image generators"

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Lee, Hyun Seung, and Manman Luo. "Transforming Creative Process: A Systematic Literature Review of Discourse on AI Image Generators." Proceedings of the Association for Information Science and Technology 61, no. 1 (2024): 986–88. http://dx.doi.org/10.1002/pra2.1162.

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ABSTRACTAI image generator, also known as Text‐to‐image generation (TTIG) technology, allows users to generate images based on natural language input. With advancements in AI, debates related to its application in the creative field have flourished. A systematic literature review was conducted to examine discourse that addresses the perception of people in the creative industry towards AI image generators and the impact of AI image generators on these individuals. The analysis of discourse revealed four major categories: 1) Evaluation of product or outcome of AI; 2) Impact of AI on practitione

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Hong, Yan, Jianming Feng, Haoxing Chen, et al. "WildFake: A Large-Scale and Hierarchical Dataset for AI-Generated Images Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 3500–3508. https://doi.org/10.1609/aaai.v39i4.32363.

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The development of text-to-image generative models has enabled the creation of images so realistic that distinguishing between AI-generated images and real photos is becoming a challenge. This progress offers new possibilities but also raises concerns over privacy, authenticity, and security. Detecting AI-generated images is crucial to prevent misuse. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake. This dataset features cutting-edge image generators, a wide variety of generator categories, and generators

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Malevé, Nicolas. "Lost in Compression." Media Theory 8, no. 1 (2024): 205–28. http://dx.doi.org/10.70064/mt.v8i1.1074.

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Recent developments of image generators have introduced a new point of contention in the already contested field of artificial intelligence: the ownership of images. In 2023 Getty Images sued the company Stability AI, accusing it of illegally appropriating photographs for the purpose of training its models. Analysing image generators and stock agencies as probabilistic systems, this text argues that their significant difference lies in their model of appropriation. Where Stability AI proceeds through direct appropriation, the stock agency proceeds through contractual appropriation using its do

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Mahajan, Mr S. K. "AI IMAGE GENERATOR THROUGH TEXT PROMT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28932.

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In the era of digital content creation, the Text-to- Image Generator has emerged as a powerful and innovative tool that transforms textual descriptions into captivating visual representations. This website leverages advanced deep learning techniques to bridge the gap between imagination and reality, enabling users to effortlessly generate stunning images from their written words. Our Text-to-Image Generator website provides a user- friendly interface where individuals can input textual prompts, whether they are vivid descriptions, imaginative stories, or abstract concepts. Behind the scenes, s

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Fareed, Mohamed W., Ali Bou Nassif, and Eslam Nofal. "Exploring the Potentials of Artificial Intelligence Image Generators for Educating the History of Architecture." Heritage 7, no. 3 (2024): 1727–53. http://dx.doi.org/10.3390/heritage7030081.

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The rapid integration of Artificial Intelligence (AI) tools, specifically text-to-image generators, across various domains has had a profound impact on numerous fields. Despite this, the potential applications of AI image generators in architectural education, particularly in teaching the history of architecture, remain underexplored. This research aims to uncover the possibilities of utilizing AI image generators, with a specific focus on the capabilities of Leonardo AI, to enhance communication and engagement. This study employed an experimental methodology to investigate how the integration

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Enjellina, Eleonora Vilgia Putri Beyan, and Anastasya Gisela Cinintya Rossy. "Review of AI Image Generator: Influences, Challenges, and Future Prospects for Architectural Field." Journal of Artificial Intelligence in Architecture 2, no. 1 (2023): 53–65. http://dx.doi.org/10.24002/jarina.v2i1.6662.

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Recently, the topic of text-to-image-based GAI (Generative Artificial Intelligence) or AI Image Generators became so popular because of its sophistication in creating images based on human natural language messages in a short time. On the other hand, the presence of the AI Image Generator is enough to reap various opinions, including in the field of architecture. Therefore, the purpose of this paper is to present a review of the influences, challenges, and prospects of AI Image Generator technology in the architectural design process. The research method used is a systematic literature review

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Hilles, Stefanie. "ARTificial Intelligence: opportunities for art librarians to engage students’ critical thinking with AI image generators." Art Libraries Journal 50, no. 1 (2025): 2–5. https://doi.org/10.1017/alj.2025.3.

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Art librarians work with images. It’s one of the things that separates us from many of our fellow subject librarians. As the academy continues to grapple with the benefits, drawbacks, and effects of AI, art librarians are uniquely positioned to teach students how to critically engage with AI image generators. Considerations concerning copyright, bias in datasets, formal analysis, and AI image generators’ potential as an art medium are some examples of topics that art librarians have at their disposal.

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Jonek, Michael, Malte Bast, and Martin Manns. "Manual assembly planning with AI Image Generators." Procedia CIRP 130 (2024): 139–44. http://dx.doi.org/10.1016/j.procir.2024.10.068.

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Ananto, Muhammad Mizan. "Legal Position Artificial Intelligence Art Generator in Copyright Law." JISIP (Jurnal Ilmu Sosial dan Pendidikan) 7, no. 4 (2023): 2938. http://dx.doi.org/10.58258/jisip.v7i4.5520.

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Artificial Intelligence (AI) art generators are disrupting the meaning of creating an artwork that has long been known. The AI art generator has features that make it easy for users to create images, simply by entering text descriptions, then the AI will produce the desired image. This creation process then raises questions about the legal position of AI on copyright contained in the image. This study discusses the legal position of AI in intellectual property law by dividing it into two options: First, how is the prospect of recognizing AI as a legal subject of copyright law, and secondly how

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Sikandar, Ayesha, and Hafiza Moatter Bahar. "A comparative analysis to assess anatomical illustrations via three AI-Driven Text-to-Image generators." European Journal of Anatomy 28, no. 5 (2024): 631–34. http://dx.doi.org/10.52083/dlqj8889.

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In recent years the popularity of artificial intelligence (AI) has increased rapidly. While the machine itself can be built for deep learning in order to enhance its own efficiency through sophisticated networks, artificial intelligence (AI) uses machine-learning models to store, calculate, analyze, and even enhance extensive volumes of data that have to be retrieved whenever needed. Text-to-image AI models like Midjourney, Microsoft Bing Image Creator Powered by DALL- E, and Craiyon can generate artistic and impressive images. In this study, anatomical representations of the human ribs, brain

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Book chapters on the topic "AI image generators"

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Pallant, Julie. "Creative Uses for AI Image Generators." In Creating Images Using AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003541677-3.

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Soma Shiva Sai Babu, G., and K. S. Rekha. "Innovative AI-Powered Image Generator: Converting Text into Images with OpenAI." In Proceedings of 4th International Conference on Artificial Intelligence and Smart Energy. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61475-0_34.

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Oppenlaender, Jonas. "The Cultivated Practices of Text-to-Image Generation." In Humane Autonomous Technology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66528-8_14.

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AbstractHumankind is entering a novel creative era in which anybody can synthesise digital information using generative artificial intelligence (AI). Text-to-image generation, in particular, has become vastly popular and millions of practitioners produce AI-generated images and AI art online. This chapter first gives an overview of the key developments that enabled a healthy co-creative online ecosystem around text-to-image generation to rapidly emerge, followed by a high-level description of key elements in this ecosystem. A particular focus is placed on prompt engineering, a creative practice that has been embraced by the AI art community. It is then argued that the emerging co-creative ecosystem constitutes an intelligent system on its own—a system that both supports human creativity, but also potentially entraps future generations and limits future development efforts in AI. The chapter discusses the potential risks and dangers of cultivating this co-creative ecosystem, such as the bias inherent in today’s training data, potential quality degradation in future image generation systems due to synthetic data becoming common place, and the potential long-term effects of text-to-image generation on people’s imagination, ambitions, and development.

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Kotha, Mahesh, Aluri Gopi, Awatef Balobaid, et al. "Conversion of Prompt to Image Generation Using AI Generator." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1267-3_21.

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Gao, Biao, Huiqin Xie, Shuangshuang Yu, Yiming Wang, Wenxin Zuo, and Wenhui Zeng. "Exploring User Acceptance of Al Image Generator: Unveiling Influential Factors in Embracing an Artistic AIGC Software." In AI-generated Content. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7587-7_17.

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Lee, Yu-Hsu, and Chun-Yao Chiu. "The Impact of AI Text-to-Image Generator on Product Styling Design." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35132-7_38.

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Lee, Yu-Hsu, and Tzu-Hsun Lin. "The Feasibility Study of AI Image Generator as Shape Convergent Thinking Tool." In Artificial Intelligence in HCI. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35891-3_36.

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Kimura, Takeshi. "Artistic Creativity in the Image AI Generator and Language as a Tool." In Philosophy of Engineering and Technology. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-77857-5_13.

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Holst, Dirk, Daniel Schoepflin, and Thorsten Schüppstuhl. "Generation of Synthetic AI Training Data for Robotic Grasp-Candidate Identification and Evaluation in Intralogistics Bin-Picking Scenarios." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_28.

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AbstractRobotic bin picking remains a main challenge for the wide enablement of industrial robotic tasks. While AI-enabled picking approaches are encouraging they repeatedly face the problem of data availability. The scope of this paper is to present a method that combines analytical grasp research with the field of synthetic data creation to generate individual training data for use-cases in intralogistics transportation scenarios. Special attention is given to systematic grasp finding for new objects and unknown geometries in transportation bins and to match the generated data to a real two-finger parallel gripper. The presented approach includes a grasping simulation in Pybullet to investigate the general tangibility of objects under uncertainty and combines these findings with a previously reported virtual scene generator in Blender, which generates AI-images of fully packed transport boxes, including depth maps and necessary annotations. This paper, therefore, contributes a synthesizing and cross-topic approach that combines different facets of bin-picking research such as geometric analysis, determination of tangibility of objects, grasping under uncertainty, finding grasps in dynamic and restricted bin-environments, and automation of synthetic data generation. The approach is utilized to generate synthetic grasp training data and to train a grasp-generating convolutional neural network (GG-CNN) and demonstrated on real-world objects.

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Gragnaniello, Diego, Francesco Marra, and Luisa Verdoliva. "Detection of AI-Generated Synthetic Faces." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_9.

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AbstractIn recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning methods it is now possible to generate visual data with a high level of realism. This is especially true for human faces. Advanced deep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic faces from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in the literature for the detection of synthetic faces. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.

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Conference papers on the topic "AI image generators"

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Sadasivam, Tanuj, Ria Treza Serrao, Aditya Prasad Bansod, Tanay Singh, and R. Bharathi. "CelestLearn AI: Astronomical Image Generator and Classifier." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673540.

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Khan, Tahsina, and Md Mehedi Hasan Emon. "Determinants of AI Image Generator Adoption Among Marketing Agencies: The Mediating Effects of Perceived Usefulness." In 2024 IEEE 3rd International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON). IEEE, 2024. https://doi.org/10.1109/raaicon64172.2024.10928548.

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Copper, Cathryn, Paul Howard Harrison, and Zhenxiao Yang. "Artificial Intelligence Literacy: Collaborating to Support Image Research in Architecture Education." In 112th ACSA Annual Meeting. ACSA Press, 2024. http://dx.doi.org/10.35483/acsa.am.112.21.

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This paper explores a collaborative approach to integrating artificial intelligence (AI) literacy into the architecture curriculum,with a particular focus on the role of architecture libraries and librarians in supporting this technology during the concept phase of design research. It outlines a student assignment that uses text-to-image AI generators to recreate architectural images and assess the role of bias in the image’s automated creation. A comprehensive online guide supports the student’s investigation of AI ethics, concept creation, prompt engineering, and evaluation. Feedback from th

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Singh, Aditi. "A Survey of AI Text-to-Image and AI Text-to-Video Generators." In 2023 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC). IEEE, 2023. http://dx.doi.org/10.1109/airc57904.2023.10303174.

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Ilieva, Julieta, Liliana Indrie, Zlatin Zlatev, and Amalia Sturza. "Integrating text-to-image in fashion accessories design." In 7th International Scientific Conference Contemporary Trends and Innovations in Textile Industry – CT&ITI 2024. Union of Engineers and Technicians of Serbia, Belgrade, 2024. http://dx.doi.org/10.5937/ct_iti24039i.

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This research used AI text-to-image generators in fashion design. By providing detailed textual descriptions, digital images are created that generate both sketches and photorealistic effects of fashion accessories with high-quality aesthetics and intricate design details. The research highlights the potential of these AI tools to assist fashion designers in creating accessories that meet design criteria and consumer needs. The limitation of AI tools, which usually create completely digital models and elements, has been reduced, and we have successfully translated them into real products. This

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Chiou, Li-Yuan, Peng-Kai Hung, Rung-Huei Liang, and Chun-Teng Wang. "Designing with AI: An Exploration of Co-Ideation with Image Generators." In DIS '23: Designing Interactive Systems Conference. ACM, 2023. http://dx.doi.org/10.1145/3563657.3596001.

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Armianu, Irina. "AI TEXT TO IMAGE GENERATORS IN EDUCATION TECHNOLOGY, THE POSTMODERN BREAKTHROUGH." In 16th International Conference on Education and New Learning Technologies. IATED, 2024. http://dx.doi.org/10.21125/edulearn.2024.0124.

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Han, Shu-Jung, and Susan R. Fussell. "Understanding User Perceptions and the Role of AI Image Generators in Image Creation Workflows." In CHI 2025: CHI Conference on Human Factors in Computing Systems. ACM, 2025. https://doi.org/10.1145/3706598.3713227.

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Arikan, Emine, and Siamak Aram. "Qualitative Study of Text-to-Image AI Generators and their Relationship with NFTs." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00046.

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Chiang, Zun-hwa, Wei-wei Su, and Ming-hsien Chuang. "Innovative AI-Drive Product Design: Leveraging Language Models and AI Image Generators - A Case Study on Insole Design for High-Heeled Shoes." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005591.

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Artificial Intelligence (AI) technologies, is the crucial topic all fields of professionals are researching in, it’s one of the icon of this era and its influence on our daily lives is significant, and continuously growing. Incessantly, noticing by designers and making its way into the world of product design. Language Models are to generate ideas and concepts as inspirations, while AI Image Generators are to create graphics as an alternative rapid prototyping. These functional tools are assisting designers in all sorts of ways, as well as Product Design Process (PDP). However, AI’s ability re

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Reports on the topic "AI image generators"

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Foundation models such as ChatGPT through the prism of the UNESCO Recommendation on the Ethics of Artificial Intelligence. UNESCO, 2023. http://dx.doi.org/10.54678/bgiv6160.

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The release into the public domain and massive growth in the user base of artificial intelligence (AI) foundation models for text, images, and audio is fuelling debate about the risks they pose to work, education, scientific research, and democracy, as well as their potential negative impacts on cultural diversity and cross-cultural interactions, among other areas. Foundation models are AI systems that are characterized by the use of very large machine learning models trained on massive unlabelled data sets using considerable compute resources. Examples include large language models (LLMs) suc

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