AI Watermarking: How It Works, Applications, Challenges

From lifelike videos and audio recordings to photorealistic images and engaging articles, AI-generated content is becoming more and more pervasive. While it can be used for good, it also opens the door to serious misuse, such as spreading misinformation, manipulating public opinion, and misleading voters during elections.

As such, verifying the provenance and authenticity of the digital content has never been more important. Doing so allows us to mitigate the potential harms of generative models. This is where AI watermarking comes into play, offering a critical tool in labeling and detecting AI-generated content, which is essential in combating its misuse.

In this blog post, I’ll explain what AI watermarking is and how it works, highlight its key applications, and explore the challenges and limitations it faces.

What Is AI Watermarking? 

AI watermarking is a technique that embeds recognizable signals (i.e., the watermark) into AI-generated content in ways that make the content traceable and protected without compromising its quality.

AI watermarks can be embedded in various ways depending on the content type:

• Text: Subtle linguistic patterns or variations.
• Images: Changes in pixel values or colors.
• Audio: Frequency shifts.
• Videos: Frame-based changes or specific encoding tweaks.

Watermarks can be classified based on two main factors:

1. Visibility
1. Imperceptible watermarks: These are not directly noticeable to humans and can only be identified algorithmically, such as slight changes in the structure of text.
2. Visible watermarks: These are overt and easily recognizable, such as a logo or text overlay on an image or video.
2. Resilience to manipulation:
1. Robust watermarks: These can withstand content alterations like compression, cropping, scaling, and editing.
2. Fragile watermarks: These are easily destroyed by any modification but are still useful for verifying the integrity of the original, unmodified content.

How AI Watermarking Works? 

Let me walk you through how AI watermarking works. At its core, AI watermarking involves two main stages: embedding (or encoding) and detection.

The embedding or the encoding process can be achieved in various ways including adding noise patterns and modifying low-order bits.

The detection process can be done by using algorithms that look for specific patterns or statistical anomalies in the generated content or even by training a machine learning model to distinguish watermarked content from non-watermarked content.

The whole watermarking process can be implemented in three main ways:

1. During the generative process itself (generative watermarking).
2. By editing already generated media (edit-based watermarking).
3. By altering the generative model’s training data (data-driven watermarking).

Each of these methods has its own unique strengths, and the choice of technique depends on the type of content and the use case.

Applications of AI Watermarking

Now, let’s focus on the key applications of AI watermarking. In the next sections, I’ll focus on intellectual property protection and provenance tracking, authenticity verification, and responsible AI use.

Intellectual property protection and provenance tracking

While reading the paper “Watermarking Makes Language Models Radioactive,” I was fascinated by its exploration of how AI watermarking can protect the intellectual property of large language models.

The study introduces the concept of “radioactivity,” showing how watermarked texts leave detectable traces used to fine-tune another model. This approach provides a way for generative AI model developers to trace authorized reuse of their AI-generated content, ensuring accountability for the use of their intellectual property.

Authenticity verification

Beyond its importance in protecting the intellectual property of generative AI models, AI watermarking also serves a key role in verifying authenticity and exposing deepfakes or manipulated content.

For me, this dual capability is what makes it such an indispensable technology. By embedding subtle, traceable markers into AI-generated content, watermarking allows us to detect manipulation and maintain trust in an online world increasingly filled with misinformation.

Responsible AI use 

Following the importance of AI watermarking in authenticity verification, I also see watermarking as an important step in encouraging responsible AI use. It makes it easier to identify AI-generated content, keeping generative AI developers and users accountable. Generative AI creators will be more mindful of how they use AI tools, ensuring that they don’t mislead audiences or engage in unethical practices.