Copyright Risks in Multimodal Generative AI: Images, Music, and Video Clips

You hit the generate button. The multimodal generative AI is a type of artificial intelligence that creates new content by combining text, images, audio, and video data to produce original-looking outputs churns out a stunning image, a catchy jingle, or a cinematic clip. It looks perfect for your brand campaign. You publish it. Then, three days later, you get a cease-and-desist letter. Or worse, you realize your competitor just used the exact same track because they have no legal claim to it either.

This isn't a hypothetical nightmare scenario. As of May 2026, this is the daily reality for creators, marketers, and developers using tools like Suno, Udio, Midjourney, and Stable Diffusion. We are living in a legal paradox where the things you create might not belong to you, but the things those creations resemble definitely belong to someone else.

The Ownership Void: Why Your AI Creation Might Be Public Domain

Let’s start with the most immediate risk: ownership. In the United States, the rules changed drastically in January 2025 when the U.S. Copyright Office released definitive guidance on AI-generated content. The ruling was blunt. If a piece of content is 100% generated by AI, it cannot be copyrighted. It falls into the public domain immediately upon creation.

The Copyright Office stated explicitly that protection only applies if a human author has determined "sufficient expressive elements." They cited the case of Kristina Kashtanova, who tried to register her comic book, Zarya of the Dawn, created using Midjourney. The office rejected the registration for the images because Midjourney operates unpredictably. You can prompt it, but you don’t control the pixels. Therefore, you aren't the "author" in the eyes of the law.

What does this mean for you? It means zero competitive moat. If you spend hours crafting prompts to generate the perfect logo or background music for your startup, and a competitor copies it verbatim, you have no legal recourse. You cannot sue them for infringement because you never owned the copyright to begin with. Your creative investment yields an asset that anyone can use, modify, or sell.

Music Generation: A Legal Minefield

The situation gets even stickier with audio. Platforms like Suno and Udio have democratized music production, allowing users to generate radio-quality tracks in seconds. But look closely at Suno's terms of service. They admit it outright: "Due to the nature of machine learning, Suno makes no representation or warranty to you that any copyright will vest in any Output."

• No Protection: You cannot copyright the melody, lyrics, or recording of a purely AI-generated song in the U.S.
• Infringement Risk: These models are trained on millions of copyrighted songs. The output often contains latent patterns from that training data.
• Double Jeopardy: You can't protect your output, but rights holders can potentially sue you if the AI output sounds too much like a song in their catalog.

Imagine generating a jingle for a local bakery. It goes viral. A major label claims the chord progression and vocal style infringe on a popular artist's work. Because you don't own the copyright, you can't license it to defend yourself easily. You're left holding the bag while the platform distances itself from the liability.

Image Generation: Economic Harm and Style Theft

For visual artists, the threat isn't just about copyright registration; it's about economic displacement. Lawsuits against Stable Diffusion highlight a specific harm: style mimicry. Previously, if a client wanted an image "in the style of" a specific illustrator, they had to hire that illustrator. Now, they feed the illustrator's work into the AI model and generate unlimited variations without paying royalties.

This creates a direct financial loss for human artists. Their distinctive styles-developed over decades-are being scraped, analyzed, and replicated by algorithms. While courts haven't fully ruled on whether "style" is copyrightable (it generally isn't), the commercial impact is undeniable. Brands are increasingly opting for cheap, unlicensed AI assets over commissioned human work, flooding the market with derivative content.

Furthermore, there is a growing trend of bad-faith registrations. Experts warn that some users are lying to the Copyright Office, claiming human authorship for AI-assisted works to gain protection. This corrupts the database, making it harder for legitimate creators to verify ownership and increasing litigation costs for everyone involved.

Video Clips: The Complexity Multiplier

If images and music are risky, video is a legal quagmire. Video generation combines multiple layers of intellectual property:

1. Cinematography: The visual composition and movement.
2. Sound Recording: The master audio track.
3. Musical Composition: The underlying song structure.
4. Performer Rights: Likeness and voice of actors or singers.

If an AI video generator produces a clip that resembles a copyrighted film scene, uses a recognizable voice, or incorporates a licensed soundtrack, you face liability across all these categories simultaneously. The legal framework for video is less established than for static media, meaning courts are still figuring out how to apply existing laws to dynamic, multi-modal outputs. One wrong move could trigger takedowns, fines, or bans from distribution platforms.

Regional Differences: U.S. vs. UK Law

Your location matters. While the U.S. takes a hardline stance against AI copyright, other jurisdictions offer different paths. Under English copyright law, specifically Section 178 of the Copyright, Designs and Patents Act 1988, computer-generated works can theoretically be protected. However, there’s a catch.

In the UK, sound recordings receive protection regardless of origin because they don't require "originality" in the same way compositions do. This distinction favors producers of AI audio masters but leaves composers vulnerable. Meanwhile, training data mining remains a gray area globally. Permanent copies of songs for training are likely infringement, but transient parameter extraction is still debated.

How to Mitigate Risks in 2026

So, how do you operate in this environment? You can't ignore the risks, but you can manage them. Here is a practical checklist for using multimodal AI safely:

• Document Human Input: Keep detailed records of your prompts, edits, and post-processing steps. If you significantly alter an AI output using traditional tools (Photoshop, DAWs), you may claim copyright on the *human-added* elements.
• Avoid Specific Styles: Don't prompt for "in the style of [Living Artist]." Use abstract descriptors like "cyberpunk," "watercolor," or "lo-fi" to reduce the risk of style-based infringement claims.
• Use Licensed Models: Look for AI platforms that partner with rights holders. Companies like Hipgnosis have started licensing catalogs for AI training. Using these models reduces your exposure to training-data lawsuits.
• Check Platform Terms: Read the fine print. Some platforms grant you commercial rights, others don't. Ensure your subscription tier covers your intended use case.
• Verify Voice/Likeness: Never use AI to clone a real person's voice or face without explicit written consent. Right-of-publicity laws are strict and separate from copyright.

Licensing mechanisms are emerging as the safest path forward. State-approved schemes and private deals between AI firms and copyright collectives are slowly building a framework for permissible use. Until then, caution is your best strategy.