Text-to-Image Services: Facts and Usage
Text-to-image generation is a powerful tool, but it also carries risks that are often overlooked. This article highlights the pitfalls of using AI image services, particularly regarding truth, rights, data, and responsibility.
Source: Own Visualization
Basics of AI Image Generation
Text-to-image services are image synthesis, not image search. A model generates a new image file based on patterns it has learned. OpenAI explicitly describes "prompt following" in its current image generation and the ability to work more precisely from chat context. However, this is still generation and not research. ( OpenAI)
If a prompt like "a female doctor in a Swiss hospital, modern ward, KSB feeling" is entered, a plausible-looking image is created, but it does not show a real ward and does not make a reliable statement about actual processes. AI images are "visual claims" – a statistical reconstruction of style, lighting, clothing, and spatial feel, not a fact. This gap between plausibility and reality is often overlooked in discussions.
The problem lies not only in errors but in their persuasiveness. A generator can provide "newspaper photo, 1990s, demonstration," but invent historically non-existent logos, uniform details, or signs. Such errors can become legally and reputational sensitive if the image is understood as documentation.
Platforms are reacting to this with disclosure requirements. YouTube requires disclosure of "meaningfully altered or synthetically generated content" if it appears realistic. ( YouTube Hilfe) This was also described in our own newsroom as a new creator "disclosure" function, explicitly for content that makes people, places, scenes, or events appear real. ( YouTube Blog) Realism is no longer proof today, but a style.
Source: viden.ai
Schematic representation of the text-to-image generation process, highlighting the role of text and image encoders.
Legal Aspects & Ownership
Many users equate "I generated it" with "I own it." This is only true on a contractual level. OpenAI states in its terms of service that users, "to the extent permitted by applicable law," own the output and OpenAI assigns rights. ( OpenAI Policies) Midjourney also states that users own the generated assets and may use them commercially, with certain exceptions. ( Midjourney Docs)
However, this is not equivalent to copyright protection in every country. In the USA, the U.S. Copyright Office holds that copyright requires human authorship and that AI-generated components are not simply considered human expression. ( Federal Register) The D.C. Circuit decided in the Thaler case in 2025 that an AI system cannot be recognized as an author and that the Copyright Act requires human authorship. ( D.C. Circuit) Reuters summarized this as a landmark ruling: purely AI-generated art without human involvement is not copyrightable. ( Reuters)
In practice, this means that while a marketing team may have the contractual right to use the output, they have no strong copyright lever to stop copycats if the image is used unchanged. Often, human design steps that go beyond pure prompting, such as composition, retouching, or typographic layout, provide a remedy.
Another point of conflict is training and IP conflicts. The dispute over training data has reached the courts. The UK High Court ruling in Getty Images v Stability AI of November 4, 2025, shows how copyright and trademark issues are being negotiated in generative AI. ( Judiciary UK) Law firms have classified the decision as a milestone. ( Mayer Brown)
For operators of image services, there are also model licenses. Stable Diffusion was released under the CreativeML Open RAIL-M license, described as "permissive" but demanding responsibility for ethical and legal use. ( Stability AI News) The license text governs the terms for the use and distribution of the model. ( Hugging Face) In short: "ownership" is often in the terms and conditions, "enforcement" depends on the individual case.
Source: canva.com
Example of a user interface of a text-to-image generator that converts an entered prompt into a visual result.
Data Protection & Transparency
In sensitive areas, text-to-image quickly becomes a data problem. A prompt can contain customer names, internal product details, or patient data. Therefore, it is important to read the privacy policy carefully.
OpenAI states that content can be used for service improvement, including training, and refers to opt-out options. ( OpenAI Privacy Policy) OpenAI offers a Privacy Center where a "Do not train on my content" request is possible. ( OpenAI Privacy Center) Midjourney also documents data collection and offers instructions for data deletion. ( Midjourney Privacy Policy) (Midjourney Data Deletion FAQ)
For example: A team creates an image series for a cardiology practice and uploads an internal PDF with patient workflow. Even without names, processes, devices, or internal forms can be confidential. In such cases, it is advisable to work with abstracted descriptions or to perform the generation locally/isolatedly.
Europe is tightening transparency regulations. The EU AI Act came into force on August 1, 2024, and will be fully applicable from August 2, 2026. ( EU Digital Strategy) The official legal basis is Regulation (EU) 2024/1689. ( EUR-Lex)
Particularly relevant for synthetic content is the transparency logic, which leads to labeling and disclosure. On December 17, 2025, the EU Commission published a first draft for a Code of Practice on the "marking and labelling of AI-generated content," with transparency rules for AI-generated content becoming applicable from August 2, 2026. ( EU Digital Strategy News) Anyone publishing advertising, political communication, or realistic-looking "documentary images" in Europe in 2026 must consider whether and how the image should be marked as synthetic.
Provenance technology, such as C2PA, addresses this: not "is it real?" but "where does it come from, who changed what when?" C2PA publishes a technical specification describing Content Credentials as cryptographically signed provenance information. ( C2PA Specification) The C2PA explainer states that trust decisions lie with the consumer and are made based on the identity of the signers and the assertions in the provenance. ( C2PA Explainer) The Implementation Guidance describes C2PA as an opt-in ecosystem. ( C2PA Guidance)
Adobe explains Content Credentials as "durable, industry-standard metadata" and compares them to a "digital nutrition label," including information on whether something was captured with a camera or generated or edited by AI. ( Adobe HelpX) The Adobe web app "Content Authenticity" is a tool for attaching and verifying credentials. ( Adobe Content Authenticity) The industry-wide initiative refers to C2PA as the specification basis. ( Content Credentials)
Provenance does not solve every problem, but it creates a verifiable chain as long as platforms do not strip metadata and an ecosystem participates. In a 2024 report, NIST described provenance tracking, labeling/watermarking, detection, testing, and auditing as combinable technical approaches against the risks of synthetic content. ( NIST Publications) The PDF report (NIST AI 100-4) explicitly details watermarking and detection logic as well as the limits of their effectiveness. ( NIST AI 100-4)
Challenges & Risks
The reality of platforms is often harsher than legal theory. Anyone who wants to make money with AI images quickly realizes: many rules are privately made and immediately effective.
Shutterstock explicitly does not accept AI-generated content for licensing in its Contributor Help Center. ( Shutterstock Help) Shutterstock justifies rejections by stating that contributors must prove IP ownership and that AI-generated content cannot be attributed accordingly. ( Shutterstock Rejection Reasons)
On the "Safety" page, the limits are even stricter. OpenAI prohibits "non-consensual intimate content" and sexual violence, among other things, in its Usage Policies. ( OpenAI Usage Policies) The debate around "nudified" deepfakes shows that this category is not academic: Reuters reported on January 6, 2026, on pressure from the British government on X over sexualized AI images. ( Reuters)
Anyone who generates "just a quick image" ends up in a situation that many only understand when things go wrong: A tool can do a lot technically, but it's not allowed to. And even if a tool allows something, the platform on which it is posted may require labeling or remove content.
A sensitive underlying issue is training data and risks of misuse. The discussion around LAION-5B is a well-known example: reports and analyses have documented that such large image-text datasets can contain problematic content. The Guardian reported on research identifying CSAM in LAION-5B. ( The Guardian) FedScoop has also addressed the topic of "tainted datasets" in the context of LAION and research risks. ( FedScoop)
This is a realistic background for policy decisions: why reputable providers use strict filters and why open-weights communities often struggle with retrofitted security measures.
Text-to-image seems like "one click," but it's computational work. Diffusion models, in particular, run the network multiple times until an image is created, which affects energy and latency. NeurIPS publications describe this character of diffusion inference as repeated network execution. ( NeurIPS)
An arXiv study from June 2025 ("The Hidden Cost of an Image") reports on an empirical experiment with 17 diffusion models and finds partly drastic differences in energy consumption – up to a factor of 46. ( arXiv 2506.17016) Another arXiv paper from November 2025 attempts to predict energy consumption via "scaling laws" for diffusion models and breaks down inference into text encoding, iterative denoising steps, and decoding, with denoising being discussed as the dominant part. ( arXiv 2511.17031)
This is relevant because costs and sustainability influence decisions: what resolution is really needed, how many variants are generated internally, how often does a team generate ten options "just to be safe" that nobody uses in the end.
Source: upscale.media
The evolution of image quality through AI generation and its influence on the art world.
Conclusion: Text-to-image is a powerful tool, but not a neutral brush. It produces convincing images without a claim to truth, shifts rights issues to terms of service and courts, turns prompts into data, and entails platform and regulatory logic.
Those who take this seriously work differently: not with fear, but with craftsmanship. Images are treated as synthetic statements, not as evidence. Publications are planned in such a way that labeling, provenance, or at least clean disclosure is possible. Before an image is monetized or used in sensitive contexts, the question is not "Do I like it?" but "Can I stand behind it – legally, data protection-wise, communicatively?".