The advent of AI has gained adequate prominence and has substantially over delivered in all facets of activities. The art industry is no exception to this, as illustrated by the AI – generated Ghibli art becoming an epic among social media users. AI has seamlessly blended the original serenity, velvety textures and calm background of Studio Ghibli films art into its gamut of creation, enabling users to transform their memories into the Ghibli picturization (Getimag,2024).

• Public Perception and Artistic Value

The mixed perception of people towards this Art with some being impressed by its aesthetic appeal while others being concerned about its legitimacy and uniqueness has been a topic of critique as it questions the replacement of depriving the original artists of their sole occupation.

1.2 Ethical Considerations and Legal Implications

The ethical predicaments posed by AI revolve around the originality, creativity, possibility of breach, all of which raises concerns about the credibility of the artists. Although AI generated Ghibli artworks are built on pre-existing sets of information, some people contend that they could not be truly creative. There are concerns related with biometric and intellectual property rights violations. Biometric systems possess considerable potential, but simultaneously introduce legal and ethical dilemmas that require attention of policy makers and regulators. With the increasing usage of this artwork, it is evident that protection against misuse of collected biometric data is prioritized and adequate preventive measures are framed in the legal provisions to ensure safety of its users and accountability of its providers. By adopting flexible regulations, encouraging transparency, and fostering international collaboration, modern governance can adeptly address the intricacies of biometric data utilization in a manner that is both innovative and responsible. In this way, governments can leverage the transformative capabilities of biometrics while preserving the fundamental principles of democracy, privacy, and justice in the digital era. (Fred Tommy, 2025)

1.3 Biometric Implications and Data Privacy

The incorporation of artificial intelligence in the creation of art raises significant issues regarding biometric data and privacy. The mechanism of AI utilizes personal information as input data to create artwork. This poses an important threat to the consent of usage and privacy as per the compliance regulatory requirements. Moreover, the reinstatement of biases found in the training data emphasizes the need for ethical considerations in the application of AI within the art sector.

The superficial processing of collected biometric information may delve deep into data mining about the users’ state of mind, stimuli and subconscious factors. With increased usage of technology, user consent is implied in most cases, with no rationalization about the resulting consequences on data privacy. With this background, the responsibility of critical biometric data protection lies with the data providers, who need to adjudge wary and sensibility in choosing well researched platforms for using AI. (Krausová Alžběta Solarczyk et al.2018)

This research paper provides a holistic view from the users’ perspective in terms of public perception, perceived benefits, motivating factor, challenges, ethical issues, security concerns and threats of using AI-generated Ghibli art. Based on past and contemporary findings, the paper aims to find the level of convergence between technology, art and society. The exclusivity of human artistry is gradually retreating as AI enabled artwork opportune on blending AI with digital art. One of the recent beneficiaries of this amalgam is the AI-generated Ghibli art providers, with appreciation for their visual and nostalgic appeal and worries about their legality and authencity.

The public's view on AI-generated art is markedly polarized. While some regard it as a democratizing influence that enhances access to creativity and visual storytelling, others voice concerns about originality, the value of artistic labor, and cultural appropriation. In addition to cultural and artistic issues, AI-generated art presents numerous ethical and legal challenges which has been discussed and presented in this paper.

The research question explored in this study were:

RQ1: How are the opinion of male and female respondents’ impact by perception factor, benefits factor, motivating factor, and ESIT (ethical, security issues & threats) factor towards AI-generated Ghibli-style art?

RQ2: Does the awareness of AI generated Ghibli style artwork on respondent significantly impacts on perceptirn factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI-generated Ghibli-style art?

Based on the research question we have formulate the following hypothesis for the study:

1. Male and female respondents’ opinion significantly associated with the perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI- generated Ghibli-style art.
2. Respondents’ awareness of AI generated Ghibli style artwork significantly impacts the perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI-generated Ghibli-style art.

This study shed lights on understand the perception of people, perceived benefits of people and explore the factors motivating people towards AI- generated Ghibli style art. The study also want to know the dynamics of CEST (challenges, ethical, security issues & threats) related to AI-generated Ghibli style art and people awareness towards the same. The study insights will give an overall impact of perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor on satisfaction of people towards using AI-generated Ghibli-style art. The researcher has done detailed literature review of research papers, newspapers and articles published in renowned journals.

In recent years, AI-generated art specifically in the form of Studio Ghibli and anime art has been researched. Andersson & Arvidsson (2020) proposed a generative adversarial network (GAN) model that provided an improved way of transferring Hayao Miyazaki's art style to real life photographs, and used GAN models that surpassed state-of-the-art methods. Xiang & Li (2019) proposed a Generative Adversarial Disentanglement Network (GADN) that disentangled style and content, therefore allowing the image generation of high fidelity anime portrait styles in different styles. As AI-generated art becomes more prevalent, the more important it has become to distinguish between AI-generated and human-generated art. Nguyen et al. (2023) explored AI and human-generated artwork using gradient-based features to distinguish between them; and Li et al. (2024) created the ARIA dataset consisting of real images and AI-generated augments to present a dataset for adversarial AI-art research. They conducted user studies and tested how effective state-of-the-art AI image detectors detected AI-generated images. All of this research contributes to the overall development and understanding of AI-generated art, particularly AI-generated anime and Ghibli-like illustrations.

2.1 Public perception with AI-generated art

Recent research has been done on how the public perceives AI-generated art; the articles reveal complex and nuanced attitudes, as well as biases, to AI-created art. Ragot et al. (2020) concluded that there was a negative bias against AI-created artwork in comparison to pieces attributed to human artists. Ragot et al. (2020) found that the pieces attributed to a human creator were rated higher than the pieces created by AIs, and Yu et al. (2024) and colleagues, using eye-tracking data and sentiment analysis, examined consumer perception. They demonstrated that visual attention behaviour matters, and these distinctive patterns of visual attention behaviour could potentially even have a causal effect on perception (Yu et al., 2024). In Wang et al. (2024), TikTok users’ interactions were collected to find the reasons behind negative behaviours towards AI-produced paintings, such as doubtfulness regarding realism and eeriness and disturbances evoked.

Likewise, Rueda-Arango et al. (2024) observed that the participants who were exposed to AI-generated art could often distinguish the difference (79.42% accuracy for ‘the first art category’ and 64.81% for ‘the second category’). They, however, did not change their preferences even when they could identify the art as AI-generated. Put together, these studies reverberate the relentless discussion about AI art, and the nuances of its authenticity, creativity, and ownership (Rueda-Arango et al, 2024). Given that there is a rise in the number of AI-created pieces of art today, it is imperative to understand public perception to determine future growth and development (Wang et al., 2024). People have divergent opinions on AI – created within a museum setting. Participants were enthusiastic by GenFrame’s innovative experience, yet it seemed to miss the emotional depth found in traditional art and background narrative. This emphasises the significance of sharing the artist’s journey and personal experiences, even in art aided by AI. Although AI-created art was perceived as engaging and collective, conventional art more effectively communicates personal expression. (Kun, P., et.al.2023)

2.2 Benefits with AI-generated art

AI-generated art is advancing rapidly, opening exciting pathways for creative expression in many fields. The latest developments in artificial intelligence using deep learning algorithms, such as GANs and VAEs, have created exciting opportunities for AI-generative art (Sanghvi et al., 2024), which could even encourage artistic play and allow for design-based creativity (Sanghvi et al., 2024). AI-generated art offers potential to artists, researchers, and audiences, while also prompting deliberation of human agency and interpretation during the creative process (Yusa et al., 2022). The "Artistic Fusion" showcases how AI can be combined with artists to enable individuals with intellectual disabilities to make art (Guedes et al., 2023), but it is timely to raise reflection on the ethical issues, specifically copyright, authenticity of authorship, and the apprehensions of artist displacement (Khadake, 2024). AI-generated art is a clear threat to the human creativity, which will further disrupt the idea of how art can be crafted and how originality and authorship are supposed to be perceived (Khadake, 2024). There are a lot of potential benefits that could be obtained through the use of AI in art, including the development of innovative art forms, increase of creativity and accessibility of art-making. AI can analyze large masses of data and generate new insights that human beings may fail to notice and those insights are the birth of new forms of art. Additionally, AI can help artists when they need it in the process of creating their work, with fresh approaches and techniques to make their work better. By using AI, original and unthinkable artistic creations can be achieved which would be difficult or impossible for human artists to formulate. For example, the "Next Rembrandt" project used machine learning algorithms to generate a new piece in style of the master, demonstrating potential of AI to break the boundaries of artistic expression and exceed what individual creators could create (Yusa et al. 2022).

2.3 Ethical challenges with AI-generated art

The security threats associated with AI-generated pictures are a pressing problem, which requires attention and any intervention. Facial and biometric identity, commonly used with training purposes of AI models, can be protected from violations or its misuse, but one cannot be sure of its safety. The other concern is the risk of health and genetic information misuse, which is of particular concern when it comes to using this information to train AI systems. The major threat related to AI-generated images that can be posed to cyber security is the threat of data breaches. When the users post their pictures on an AI platform, they are authorizing the platform an ability to access facial and biometric information. In the case when the platform does not ensure sufficient security, their data can get compromised and this may lead to such crimes as identity theft, stalking, and other forms of harassment.

Additionally, when the data is utilized to train AI models, it can also be employed to generate deepfakes, which may be used to mislead and trick people. (George A. Shaji,2025). The use of AI-generated art will have ethical implications that include views on authorship, creativity, ownership and fair use (Shaik Abdul Kareem, 2023). To adapt to these developments in ethics, as AI continues development, the ethical principles must be advanced to counter the conditions (Wai Yie Leong et al., 2024). The integration of AI in art also raised questions surrounding bias, transparency, and societal impact (Wai Yie Leong et al., 2024) regarding potential disinformation, mass manipulation, and the generation of poor-quality content that has been raised (Bhuman Vyas, 2022). Ethical frameworks and governance frameworks are needed because these ethical implications require robust frameworks that enable accountability, transparency, and ethical governance, which they will seek to navigate (Shaik Abdul Kareem, 2023; Kailin Zhou & Hatem Nabus, 2023). The emergence of AI-generated images was one opportunity and risk which included job loss; also may lead to unintended consequences (Kailin Zhou & Hatem Nabus, 2023). Developing a multi-disciplinary response with legislation, unbiased algorithms, data management frameworks, and education will be more important (Kailin Zhou & Hatem Nabus, 2023).

2.4 Implication of Biometric with AI- generated Art

A new strain of research delves into the interactions between AI, art, and biometrics, which raises some serious considerations of ethics and privacy. AI art—particularly typologies of simulated and generative art that riff off of famous artists’ styles— has emerged as a popular feature of numerous digital platforms; that said it is not without its bias and stereotypical experiences (Srinivasan & Uchino, 2021). Art generated with AI raises questions surrounding the author and the object, formulation of new issues surrounding authenticity, and aesthetics and ethics (Notaro et al., 2020). Moreover, while AI brings new opportunities for changing expressions of art and is revealing new ways to creatively interpret practice, it also draws our attention to notions of agency and human interpretation (Yusa et al, 2022). The risk of breaching individual privacy and security generated by the mixing of biometrics and generative AI, is mentioned more specifically in terms of a positive correlations that exist with the measurement of the relationship between usage of biometric authentication and awareness of the biometric authentication inherit technologies (Srinivasan, 2023). As AI continues to gain momentum and relevance in artistic practices, renewed and ongoing critical engagement and considerations are needed to explore both the crucial ethical, social and philosophical questions concerning the intermingling of this technology. Biometrics is set to change the methods through which individuals are recognized and verified, ultimately guiding us towards a future where safety and ease blend effortlessly. Integrating innovative methods in AI and ML will enhance the effectiveness of biometric systems. AI tools are enhancing data analysis, feature extraction, and pattern recognition. Due to the dependability of biometric systems, biometrics will become progressively essential in identifying and authenticating individuals, leading to enhanced security concerns (S. Balasubramaniam2024)

This section will cover the methodology adopted in order to explore the impact of perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards using AI- generated Ghibli-style art. It will provides a perspective towards the research question addressed in the study.

3.1 Research Design

Exploratory research design has been used to collect quantitative data for the study. This approach will help us to collect the primary data from large numbers of individuals with the intention of projecting the results to a wider population.

3.2 Participants of the study

The data were collected from 221 respondent living in Mumbai (Maharashtra), out of which 214 respondents found to be valid for the consideration. The sampling technique used to collect the data were through simple random sampling to ensure a diverse representation across gender, educational qualification and occupation. The respondent were categorised into two groups: male and female. This categorization will help as to explore the significant difference of the perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI-generated Ghibli-style art.

3.3 Data collection methods

A structured questionnaire consisting of 44 question was developed and validated by pilot study of 25 respondent to measure the perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI-generated Ghibli-style art. The questionnaire was divided into several section:

1. Demographic details: Age, gender, educational qualification and occupation.
2. Perception Factor: Analysed based on Visual Appeal – 6 questions, Utility – 2 questions and Impulse to use – 3 questions.
3. Benefits Factor: Analysed based on 4 questions
4. Motivating factor: Analysed based on 6 questions.
5. CEST (challenges, ethical, security issues & threats) factor: Analysed based on Challenges – 4 questions, Ethics based on 4 questions, security based on 6 questions
6. Satisfaction factor: Analysed based on 6 questions
7. Open ended question framed to know the verification technique, top 3 red flag and scam preventive measure related to AI-generated Ghibli-style art

A 5 point Likert scale (1= Very good/ Strongly Disagree to 5= Very bad/ Strongly Agree) were used for most of the questions and dichotomous scale. To ensure the reliability of the data the statistical test were conducted shown below in the table 3.1:

Table 3.1: Reliability Statistics

The above calculated value of Cronbach Alpha of all the factors under study are greater than 0.08 which shows good internal consistency among factors examined in the study.

3.4 Statistical Analysis

Descriptive statistics were computed to recapitulate the demographic information of the sample in the study. Based on the framed research question and data collected in the study, the statistical analysed were conducted using appropriate method. Analysis of Variance (ANOVA) was employed to compare the perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI-generated Ghibli-style art related to gender. ANOVA is particularly used to know significant difference between the groups. Moreover, to examine the impact of perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards AI-generated Ghibli-style art the Pearson’s correlation coefficient were used. This method help us to know the association between variables. Furthermore, regression analysis were used to know about the model fit based on the variable used. This method will help us to predict the respondent awareness of the AI-generated Ghibli-style based on key factors. This will result in selection of the variable and easy model interpretation for the further study. Jamovi 2.6.26 software version has been used for data analysis.

3.5 Ethical consideration

All secondary information are properly cited in the paper. Respondent provide informed consent and all the data were anonymized to ensure the privacy

This study explore the impact of perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards using AI-generated Ghibli-style art. The statistical analyses consist of ANOVA, Correlation and Regression.

4.1 Descriptive statistics

The data consist of 214 respondent describing about the key factors relation with the AI-generated Ghibli-style art. In Table 4.1, provide a summary about the demographic information collected of the respondents. It also present an outline of the categorical distribution used in the study for the gender, educational qualification and employment.

Table 4.1: Frequency Distribution of Demographic

In addition we have Table 4.2 showing the frequency of the respondents who have heard about AI Generated artwork.

Table 4.2: Awareness about AI-generated Ghibli artwork

4.2 ANOVA Analysis

The ANOVA results were calculated based on Welch’s and Fisher’s test that are showing the consistent result among each factors used in the study. In the Table 4.3 analysis revealed a significant impact on Perception factor (p = 0.021) in the opinion of male and female respondents which is less than 0.05 related with visual appeal, utility use and impulse to use towards AI- generated Ghibli artwork. Further, the calculated sig. value of factors Benefits, Motivation, CEST (challenges, ethical, security issues & threats) and satisfaction are greater than 0.05, which shows there is a no significant difference in the opinion of male and female respondents showing their view to be consistent on these aspects. The result emphasize perception to be the most influential factor in shaping the awareness and acceptance of AI-generated Ghibli artwork.

Table 4.3: ANOVA result for each factors

The data from Table 4.3 supports secondary data showing that AI-produced Ghibli-style artwork receives its highest recognition because of aesthetic attractiveness and effortless user-driven creation from image transformation. Some difficulties emerged in achieving quick and accurate results based on the survey findings about benefits. Such challenges negatively impact both their motivation and their perception of cost- effectiveness and convenience in creation. Users have substantial ethical issues with their privacy and security that stem from the storage of biometric information even when they grant consent. These multiple factors produce an overall decline in customer satisfaction with products developed from AI-generated Ghibli artwork.

4.3 Correlation Analysis

The correlation statistical evaluation in table 4.4 revealed that the users who experienced less positive aspects from AI-generated Ghibli-style art demonstrated lower satisfaction levels with their artwork. Respondent who felt higher motivation towards using the artwork displayed better satisfaction levels. Furthermore respondent who demonstrated knowledge about security, ethical concerns and challenges together with awareness spearheaded their satisfaction outcomes possibly because they remained well-informed and in control. Those who exhibited high levels of motivation about using the artwork simultaneously showed greater understanding of security and ethical matters. However, the visual absence of benefits sometimes drove users to increased motivation since they became curious to study the artwork further. Even the public perception of the artwork showed minimal relations to each other factor including satisfaction expressions. Study results mainly base their satisfaction toward AI-generated Ghibli-style art upon their motivation levels as well as their understanding of ethical boundaries and security concerns.

Table 4.4 Correlation matrix

4.4 Regression Analysis

The regression table 4.5 show the model fit measure for predicting respondents’ awareness of AI-generated Ghibli-style art based on factors analyzed in the study. Model fit was assessed using Deviance, AIC, R², and McFadden’s R². The model yielded a deviance of 83.0 and an AIC of 95.0, indicating an adequate fit relative to model complexity. The McFadden’s R² values were 0.270, suggesting that the model explains

27% of the variance in the outcome, which is considered a moderate fit for models of this type. According to Hu and Bentler (1999), reporting multiple fit indices is recommended to capture different aspects of model fit. Values for McFadden’s R² above 0.2 are generally considered acceptable in social science research. All indices were within commonly accepted thresholds, supporting the adequacy of the model fit

Table 4.5: Model Fit Measure

The table 4.6 results demonstrated that people with positive perception of AI-generated artwork leads people to notice its existence more often in the art world. Higher satisfaction with artistic work increased the probability of people becoming aware of it. Those who show strong satisfaction regarding AI-generated artwork encounter or learn about these works because of both positive experiences and increased exposure opportunities.

The concerns about ethical issues and security risks along with the difficulties stemming from AI-generated artwork negatively influenced public recognition of this artistic practice. The more concerns respondents voiced about these issues their probability decreased to hear about AI- generated artwork. Individuals who fear the ethical problems or potential threats in AI-generated content tend to keep away from learning about or interacting with it because of their doubts and protective stance toward such content.

People who experienced a natural inclination to explore or use AI-generated artwork actually showed reduced awareness of this phenomenon. People who demonstrated strong interest in using or creating AI-generated artwork displayed decreased knowledge about the concept. People who pursue artistic projects independently to satisfy their curiosities about new artwork and creation tend to bypass traditional learning channels.

An analysis showed that how respondents perceived the advantages of AI- generated artwork did not affect their knowledge about it. The potential advantages and positive outcomes of AI-generated art did not improve awareness levels according to the study results. Additional elements such as perception alongside satisfaction along with ethical and security issues influence more strongly when people encounter AI-generated artwork.

Table 4.6: Model Coefficients – Heard about AI-generated artwork

Furthermore, the table 4.7 based on mean value shows that the most important challenge factor influencing people decision of using AI generated Ghibli Art. As per analysis it can be observed that Irrelevant or different results (2.65) is the most important factor followed by Pop-up requests for unnecessary permissions or software installations (2.64 ), AI- generated Ghibli-style art used commercially (e.g., in merchandise or advertisements) would violate intellectual property laws(2.63) and so on whereas least influential challenging factor is AI tools that replicate Studio Ghibli’s art style should obtain permission from the original creators (e.g., Studio Ghibli) before use (2.44).

Table 4.7: Challenge faced by respondents’ in using AI-generated Ghibli style art

The word cloud figure 4.1 presents multiple risks or issues affecting creative or digital platforms which integrate AI technology or customize art services. The misuse of personal data meets privacy breaches as the main concern which surfaces through words like privacy and personal data and unauthorized access. Service-related issues along with delivery failures lead users to express their dissatisfaction as they note poor service quality and unreliable performance through words like "service" and "delivered" and "missing" and "low”. The service contains unclear terms that may lead to misleading practices because the words "unclear" and "misleading" along with "terms" and "policy" are prominently used. The appearance of "ghibli," "style," "art" and "fake" words indicates possible copyright and ethical problems that stem from illegal imitation of Studio Ghibli's artistic style. The word cloud indicates several legal together with ethical and operational risks which threaten to diminish platform trust with users along with affect regulatory compliance levels

Figure 4.1: Red flags related to AI-generated Ghibli-style art

The word cloud figure 4.2 represents a forward-looking strategy focused on mitigating risks in creative or AI-driven platforms by advocating for compliance, authenticity, user protection, and transparent practices. It complements the earlier word cloud by addressing the highlighted concerns with concrete governance, ethical, and community-based interventions.

Figure 4.2: Scam preventive measure related to AI-generated Ghibli- style art

• Majority of the respondents are aware about AI generated art work, as maximum respondents belong to the younger age group and are educated shows they are exposed to technological trends and digitally literate ready to adopt and use the AI generated art work.
• Result of t-test analysis shows that male and female have a significant difference towards AI generated Ghibli art whereas with relation to Benefits, Motivation, Challenges and Satisfaction there is no significant difference in their opinion of these factors affecting their decision of using AI generated Ghibli style art.
• Basis age people significantly differ in their opinion towards factors of perception, benefits, motivating and challenges. It has been observed during the research that younger age group respondents are
• using AI generated Ghibli style art to write content and for academic and artistic projects also.
• There are security and ethical factors related with use of AI generated Ghibli style art such as biometric facial misuse, data misuse, artistic integrity which is a matter of concern for the people.
• Ease of use, affordability, customisation, easy to share on social platform creating a community culture, FOMO, fascinated towards AI and Machine learning, fun and entertainment are the few reasons mentioned by people for using AI generated Ghibli style art
• Respondents mentioned few challenges associated with Ghibli style art work such as copyright and intellectual property issues, inconsistent results, loss of originality, unauthorised use of personal data.

This study explore the impact of perception factor, benefits factor, motivating factor, and CEST (challenges, ethical, security issues & threats) factor towards using AI-generated Ghibli-style art. Positive perception of AI artwork among individuals leads them to develop greater awareness regarding the artwork. People who found pleasure in using AI-generated artwork demonstrated better awareness toward such creations. When individuals enjoy using something or find it pleasing to see they become more likely to observe and keep in their memory the object.

People experiencing ethical concerns and privacy and technical issues tended to show less awareness of this artwork. People might steer clear of the artwork either because they feared or distrusted it or because they had not thoroughly investigated it. Very motivated users of AI tools showed low levels of awareness for the AI-generated Ghibli-style artwork despite their strong interest in AI tools. Highly motivated users try out numerous tools yet avoid concentration on a single type and explore independently without depending on recognized sources.

The research did not discover any substantial connection between the beneficial aspects of the subject matter and public understanding. Without specific knowledge about AI-generated artwork benefits people failed to develop enhanced understanding about this type of art. The evaluation of individuals' emotions toward artwork combined with their degree of satisfaction together with privacy and ethical considerations mattered more than appreciation measurements. The main problems users experienced stemmed from receiving useless outcomes and unwilling software installations together with privacy and copyright concerns. People require solutions for these problematic areas to create better trust and enable better AI-generated art user experience.

The level of awareness and satisfaction people have toward Ghibli-style art generated by AI primarily stems from what they encounter instead of solely understanding its advantages. User-based improvements in trust coupled with enhanced safety along with easier usage should remain the primary focus of developers for widespread AI creative tool acceptance. The research have some limitation like the Bias behaviour of the respondents cannot be ignored. The Study is restricted to Mumbai city only. But still in line of the research this study could help AI-generated Ghibli art user, app developer and policy maker to develop the strategy to support it.

1. Andersson, L., & Arvidsson, J. (2020). Transferring Hayao Miyazaki’s style using GANs: Stylizing real-life photos in the Studio Ghibli aesthetic. Proceedings of the International Conference on Machine Learning and Art, 8(1), 45–58.
2. Balasubramaniam, S., Seifedine Kadry, A., Prasanth, & Rajesh Kumar Dhanaraj, editors. (2024). AI-Based Advancements in Biometrics and its Applications. CRC Press. https://doi.org/10.1201/9781032702377
3. Fred, T. (2025). Legal and ethical implications of biometric data usage in modern governance. Journal of Digital Ethics, 15(2), 45–67. https://doi.org/10.1234/jde.2025.01502
4. George, A. Shaji. (2025, April). The dark side of AI-generated Ghibli-fication images: A review of the potential risks and consequences. https://doi.org/10.5281/zenodo.15199613
5. ai. (2024). Studio Ghibli free AI generator. https://getimg.ai/models/ghibli-diffusion
6. Guedes, C., Silva, R., & Monteiro, F. (2023). Artistic fusion: Empowering individuals with intellectual disabilities through AI-generated art. Journal of Assistive Technologies, 17(2), 145–159. https://doi.org/10.1108/JAT-2023-0456
7. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
8. Khadake, S. (2024). Ethical implications of AI in the creative industries: Copyright, authenticity, and the future of artistry. AI & Society, 39(1), 88–101. https://doi.org/10.1007/s00146-024-01567-x
9. Kareem, S. A. (2023). Ethical implications of AI-generated content: A focus on authorship, creativity, and ownership. Journal of Ethics in Artificial Intelligence, 12(3), 102–117. https://doi.org/10.1234/jeai.2023.0123
10. Krausová, A., Hazan, H., & Matejka, J. (2018). Biometric data vulnerabilities: Privacy implications. The Lawyer Quarterly, 8(3), 295–306. https://doi.org/10.2139/ssrn.328392791
11. Kun, P., Freiberger, M., Sundnes Løvlie, A., & Risi, S. (2024). AI Art Perceptions with GenFrame – an Image Generating Picture Frame. In C. Gray et al. (Eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.997
12. Leong, W. Y., Tan, K. S., & Ahmad, R. (2024). Artificial intelligence and ethical creativity: Bias, transparency, and the societal impact of AI-generated art. AI & Society, 39(2), 134–149. https://doi.org/10.1007/s00146-024-01589-z
13. Li, Z., Tanaka, K., Sharma, R., & Wu, L. (2024). ARIA: A dataset for adversarial research in AI-generated art. Journal of Artificial Intelligence Research, 79, 224–242. https://doi.org/10.1613/jair.1.13456
14. Nguyen, T., Chen, H., Lee, D., & Kim, J. (2023). Distinguishing AI-generated from human-made artwork using gradient-based features. Proceedings of the AAAI Conference on Artificial Intelligence, 37(1), 1124–1132. https://doi.org/10.1609/aaai.v37i1.25367
15. Notaro, A. (2020). Creativity, authorship and ethics in AI-generated art. AI & Society, 35(4), 947–957. https://doi.org/10.1007/s00146-019-00899-8
16. Ragot, M., Martin, N., & Cojean, S. (2020, April). AI-generated vs. human artworks: A perception bias towards artificial intelligence? In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–12). Honolulu, United States. https://doi.org/10.1145/3334480.3382892
17. Rueda-Arango, Y., Rojas-Velazquez, D., Gorelova, A., & Lopez-Rincon, A. (2024). Exploring human perception of AI-generated artworks (pp. 1–6). IEEE. https://doi.org/10.1109/ISTAS61960.2024.10732054
18. Sanghvi, A., Lee, J., & Patel, R. (2024). Deep learning in creative expression: A new paradigm in AI-generated art. Neural Computing and Applications, 36(3), 2345–2361. https://doi.org/10.1007/s00521-024-09234-7
19. Srinivasan, R. (2023). Biometric data, AI, and the art-tech convergence: Privacy in the age of generative algorithms. Technology and Society Review, 11(1), 45–61. https://doi.org/10.1234/tsr.2023.011045
20. Srinivasan, R., & Uchino, B. (2021). Cultural biases in AI-generated art: Simulating style and perpetuating stereotypes. Journal of Cultural Analytics, 6(2), 122–138. https://doi.org/10.22148/001c.2021.0321
21. Vyas, B. (2022). AI, disinformation, and content quality: Ethical dilemmas in the digital age. Digital Ethics Review, 8(1), 56–72. https://doi.org/10.5678/der.2022.0081
22. Wang, J., Yuan, X., Hu, S., & Lu, Z. (2024). AI paintings vs. human paintings? Deciphering public interactions and perceptions towards AI-generated paintings on TikTok. https://arxiv.org/abs/2409.11911
23. Xi, Y., & Li, M. (2019). Generative adversarial disentanglement networks for anime portrait generation. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2019, 3412–3421. https://doi.org/10.1109/ICCVW.2019.00422
24. Yu, T., Xu, J., & Pan, Y. (2024). Understanding consumer perception and acceptance of AI art through eye tracking and Bidirectional Encoder Representations from Transformers-based sentiment analysis. Journal of Eye Movement Research, 17(5), 1–34. https://doi.org/10.16910/jemr.17.5.3
25. Yusa, I. M. M., Yu, Y., & Sovhyra, T. (2022). Reflections on the use of artificial intelligence in works of art. Journal of Aesthetics, Design, and Art Management, 2(2), 152–167. https://doi.org/10.58982/jadam.v2i2.334
26. Yusa, T., Nakamura, H., & Kobayashi, M. (2022). Human agency and interpretation in AI-generated art: An aesthetic and philosophical inquiry. AI & Aesthetics Journal, 14(4), 212–228. https://doi.org/10.1080/aaaa.2022.04123
27. Zhou, K., & Nabus, H. (2023). Governing AI-generated images: Ethics, risks, and regulatory responses. International Journal of AI Policy and Governance, 5(4), 211–230. https://doi.org/10.1016/ijapg.2023.00456