AI-Generated Images and Copyright — Watermarking Solutions
AI image generation technologies like Stable Diffusion, Midjourney, and DALL-E have evolved rapidly, enabling anyone to create high-quality images easily. However, this technological advancement has created new copyright challenges. This article examines the current state of copyright issues surrounding AI-generated images and explores how watermarking technology can help address them.
The Current State of AI Image Generation
AI image generation uses deep learning models trained on massive image datasets to create new images based on text prompts. Since 2022, high-performance models like Stable Diffusion, Midjourney, and DALL-E 3 have been released in succession, enabling non-artists to create photo-quality images and illustrations in seconds. However, these AI models use vast amounts of internet images as training data, including copyrighted works by creators. This has sparked significant legal and ethical debates. In Japan, as of 2025, Article 30-4 of the Copyright Act permits the use of copyrighted works for information analysis to a certain extent, but clear precedent regarding generated content that resembles existing works has not yet been established.
Three Copyright Challenges of AI-Generated Images
1. Copyright Ownership of AI-Generated Works
The question of whether AI-generated images receive copyright protection, and if so, who holds that copyright. Under Japanese copyright law, works are defined as 'creative expressions of thoughts or sentiments,' and AI itself cannot hold copyright. However, if a user's creative involvement in prompt design and refinement is recognized, copyright may be granted depending on the degree. Currently, the prevailing view is that fully AI-generated images do not receive copyright, but the debate continues regarding how much creativity in prompt engineering should be recognized.
2. Copyright Infringement in Training Data
Image datasets used to train AI models contain many copyrighted works. In Japan, Article 30-4 of the Copyright Act generally permits the use of copyrighted works for information analysis, but the scope of this provision is debated. Particularly when generated images mimic the style or composition of training source works, potential copyright infringement has been flagged. Internationally, multiple artists have filed class-action lawsuits against AI companies.
3. Deepfakes and Misinformation
AI image generation can create extremely realistic images of real people and places, creating risks for deepfake abuse. Fake political statements, synthesized celebrity photos, and fabricated news images can cause social disruption. Technologies to identify AI-generated content are becoming increasingly important to combat the spread of misinformation.
International Trends in Content Authentication
To address the increase in AI-generated content, international efforts are underway to prove content provenance and authenticity. The Content Authenticity Initiative (CAI), founded in 2019 by Adobe, Twitter (now X), and The New York Times, promotes open standards for proving digital content origins and provenance. Over 1,500 organizations now participate. The Coalition for Content Provenance and Authenticity (C2PA), as the technical standards arm of CAI, has developed the C2PA Manifest specification for recording content provenance information. This specification is being adopted by camera manufacturers and software companies alike. Watermarking plays a vital role in these efforts. While metadata like C2PA Manifests can be deleted, watermarks embed information directly in images, maintaining provenance even when metadata is lost.
Watermarking Solutions for AI Image Issues
1. Protecting Original Works
By embedding watermarks in their works, creators can establish evidence of original authorship when AI-based unauthorized learning or imitation occurs. Using truvis to pre-embed watermarks in portfolio and social media images secures proof of ownership. Even if similar AI-generated images appear, detecting the watermark in the original proves prior creation.
2. Identification Marking of AI-Generated Images
Efforts are advancing to embed watermarks in AI-generated images to enable later identification. Google DeepMind's SynthID, OpenAI's DALL-E 3 built-in watermark, and Meta's Stable Signature are examples of major tech companies developing AI image identification watermarks. This is expected to facilitate detection of deepfakes and misinformation.
3. Maintaining Content Provenance
C2PA provenance information is recorded as metadata, but metadata can be deleted during image resaving or social media posting. Watermarks embed information directly in pixel data, leaving provenance clues even when metadata is lost. Combining C2PA metadata with watermarks provides more robust content authentication.
Actions Creators Can Take Now
There are practical steps creators can take immediately to protect their work in the AI era. First, always embed watermarks before publishing your work. truvis lets you embed invisible watermarks for free. Second, record the publication date of your work — social media timestamps and file metadata serve as evidence of creation timing. Also, store original high-resolution files in a secure location and maintain a mapping table of watermarked images and their payloads. Additionally, perform regular reverse image searches to check for unauthorized use. Google Image Search and TinEye are useful tools for this purpose.
Summary
The evolution of AI image generation is forcing fundamental changes in copyright protection. While legislation has not fully caught up, watermarking technology is a practical protection tool creators can use right now. Use truvis to embed watermarks in your work and prepare for copyright protection in the AI era.