## 5 Discussion Results
These products were consistently rated poorly by both experts and lay participants, not because they were entirely unrealistic, but because they were _almost_ correct in form, yet subtly off in critical ways (e.g., color, cross-section details, or texture). This near-realism likely triggered a perceptual dissonance similar to that observed in uncanny valley responses, where images that closely mimic real objects, but fall short in subtle ways, can evoke discomfort or distrust. In food imagery, such discrepancies may undermine appetitive responses, particularly for processed meats where visual cues strongly signal quality and safety. Recognizing this effect is essential for improving GenAI tools in domains where realism is a key trust factor. A key technical insight from this study is that using reference photographs during AI image generation greatly improved visual fidelity and appeal. The burger and roasted chicken images, both created with sample photos, received the highest preference ratings (92.9%), showing that combining visual references with textual prompts helps GenAI better replicate crucial features like texture and color. This hybrid approach offers a practical solution for professionals aiming to balance aesthetic quality with product recognizability. Moreover, cross-cultural differences in product perception were observed among survey participants. Individuals from different countries may hold varying expectations of how a given product should appear, which complicates the question of whether AI-generated images can achieve universal acceptance. Future research should therefore investigate how cultural backgrounds influence the reception of such content and how generative models might incorporate regional specificities. In addition to technical and perceptual considerations, the ethical implications of using AI-generated food imagery deserve closer attention. As AI systems continue to evolve and approach greater levels of visual accuracy, particularly in terms of texture, structure, and culturally specific visual cues, it becomes increasingly important to implement robust regulatory practices. One of the most pressing issues is the transparent labeling of AI-generated images. Without a clear indication, consumers may be misled about the authenticity of the products depicted, especially when dealing with traditional or protected foods where visual characteristics are tightly linked to quality and origin. Proper labeling not only preserves consumer trust but also upholds ethical standards in advertising and product communication. As generative models improve, closing the gap between real and synthetic images, the responsibility to differentiate the two will grow accordingly. Moreover, future standards should address whether watermarking or provenance metadata is preserved in AI-generated images, as such mechanisms may play a crucial role in maintaining consumer trust once disclosure becomes mandatory. Ensuring visual appeal does not override transparency will be key to fostering responsible innovation and ethical communication in the food sector. Looking ahead, future AI development may significantly reduce the current limitations in structural accuracy, textural realism, and visual consistency. Advances in multimodal training, higher-resolution modeling, and incorporation of photographic reference data could allow AI systems to generate food images that are nearly indistinguishable from real photographs. Such progress would expand the practical utility of generative models, not only for marketing but also for product design, education, and labeling, provided that ethical and regulatory safeguards evolve alongside these capabilities. In summary, both studies indicate that AI-generated images of meat products still do not attain the professional precision and authenticity afforded by real photographs; as a result, traditional photography remains superior in certain contexts.