Protecting AI generated art with watermarking techniques

Everyone is stressed about the ownership of digital art.

There is a lack of robust legal frameworks that effectively recognize and protect digital creations.

The intellectual property of generative AI technologies like latent diffusion models and GANs remains a big concern for most artists.

Watermarking digital art seems to be one of the paths generative AI will take.

It uses sophisticated algorithms to embed invisible watermarks.

These marks are undetectable to viewers but can be recognized by software, It provides a robust method for authentication and copyright management.

As generative AI technologies like latent diffusion models and GANs revolutionise content creation, the intellectual property remains a big concern for most artists.

These invisible, embedded markers assert copyright while tracking and controlling content distribution.

1) Understand the watermarking methods

Two primary watermarking methodologies exists:

• Model Learning Specific Data Distribution: This strategy involves training generative models to embed a unique data pattern recognizable by another model. This pattern serves as the watermark, ensuring each piece of content can be traced back to its source. In this method, the watermark is integrated in the generative model.

• Post-Generation Watermark Embedding: After content is generated, a separate watermark embedding model appends a unique marker to the content. This method is similar to traditional digital watermarking but adapted for AI-generated assets. In this method, the watermark is not integrated in the generative model, it is a post-process.

'There are 2 significant vulnerabilities in watermark techniques:

• Watermark Removal: The most used technique consists in apply denoising algorithms to the image. The denoising can strip these embedded watermarkers, erasing ownership traces without detection.

• Watermark Forgery: It consists in create illegal content with forged watermarks from another user, causing the service provider to make wrong attributions. Leading to misattribution and potential framing of innocent creators.

The removal of watermarks can be achieved through various sophisticated techniques:

• Image Inpainting: Modifying an image to remove visible watermarks by filling in the watermark area with new image data that blends seamlessly with the surrounding pixels.

• Denoising: Uses a diffusion model to introduce noise into the watermarked data and then applies denoising to distort and effectively erase the watermark, making it unrecoverable.

• Disrupting Methods: These involve altering the data in a way that disrupts the watermark's pattern without trying to reconstruct the original image data, making the watermark undetectable.
  

2) Breakdown of Watermark Removal Attack

Here's how the watermark removal attack unfolds:

1. Data Collection: Collecting watermarked data from AI-generated content services or user-shared online content.

2. Data Pre-processing: Applying a pre-trained denoising model to add noise and then clean the images, distorting the embedded watermark beyond recovery.

3. Model Training: Training a Generative Adversarial Network (GAN) to learn the mapping from distorted, denoised data back to its original form, minus the watermark.

3) Implications for VFX Artists

Understanding the steps involved in watermark removal and the vulnerabilities of current watermarking methods is crucial for VFX professionals.

This knowledge helps in not just applying watermarking for IP protection but also in designing more robust methods that can withstand sophisticated attacks.

Staying engaged with cutting-edge research and contributing to discussions on security innovations is essential.

Stay creative, stay secure.

Thanks for reading and enjoy the rest of your day,

-Oriol