Category: AI-Generated Content Detection

As AI-generated content becomes smarter and more realistic, the question of how to tell what is real becomes increasingly difficult to answer. Watermarks are often used as a telltale sign that clearly identifies AI-generated media, giving us confidence in what we are seeing. However, in reality, they are far less reliable than they appear (or not appear).

First, you need to know what a watermark is

Watermarks are labels used to show that a piece of content was generated by AI. The goal is to give some indication of where the content came from, especially as AI-generated images, videos, audio, and text become more common online.

There are three different types of watermarks: visible, invisible, and metadata.

Visible Watermarks 

Visible watermarks are logos or text placed directly on the image or video to indicate that it was created by AI from a specific generator. They are easy to understand, but they are also very easy to take off. A visible watermark can be cropped out or removed by running the media through another AI generator. Once it is gone, it will be very hard for an individual to pinpoint whether it is fake.

Invisible Watermarks 

Some companies use invisible watermarks that are embedded into the pixels of an image or video when it is synthesized, like Google’s SynthID, following a specific mathematical pattern.

These watermarks are more resilient than visible ones, but they still have limits. Only the company that created the AI-generated content can reliably detect it, which means an image or video has to be checked against multiple systems to figure out where it came from. 

Edits can also weaken the invisible watermark. Adding emojis, text overlays, heavy filters, or regenerating the media through another model can damage or remove it. 

Overall, even when detection works, it only confirms that an image or video was developed by a specific company when checked using that company’s own system, not that it is AI-generated in a broader sense.

Metadata Watermarks

Metadata-based watermarks do not change how content looks. Instead, they add extra information to the file that explains where it came from and how it was made.

One example is C2PA. C2PA adds a record to an image or video that shows its creation and editing history, such as which tools were used and whether AI played a role. This record is designed to stay with the media as it is shared, so people can check its background.

The downside of metadata-based watermarks is that they are easy to lose. The added information can be removed on purpose. Sharing an image or video through messaging apps, social platforms, or simple file conversions can remove this information by accident.  

Overall, there is no single watermarking system used across the industry. Different companies use their own methods. However, watermarks should not be thought of as a reliable indication for AI-generated content. They are better understood as an additional layer of detection, but it should be acknowledged that they may not survive once a piece of content is shared or edited.

So if not watermarks, then what actually works

Since watermarks can be inconsistent, then how can AI-generated content be detected at all?

The answer is not another kind of label. Detecting AI-generated content today requires systems that can analyze the output itself rather than checking for markings that are not always present.

Every generator is built on a specific architecture, and that process leaves behind patterns in the output, even when the image or video looks realistic to the human eye. These patterns are tied to how the model generates media, not to any watermark that was added afterward. Content produced by the same model tends to share subtle characteristics that appear consistently across many outputs.

This is what detection systems are designed to identify and why they are so important.

How AI detection systems work

AI detection systems are trained by comparing large sets of real images or videos with AI-generated ones. By doing this at scale, they learn which signals tend to show up in synthetic content and which do not, even when media looks convincing to a human viewer.

At Hive, we take this approach by training our detection systems to analyze images and videos directly. Instead of looking for a single obvious tell, our models learn many subtle signals across content developed by a wide range of AI models. Training on this mix of real and synthetic content allows our systems to recognize AI-generated media from new or unfamiliar generators, even before we have explicitly trained on those specific models.

Because this approach is based on how content is generated rather than on labels or markings, it holds up in real-world use. Detection still works when watermarks are missing, metadata has been stripped, content has been edited or re-uploaded, or the source model is unknown or proprietary.

As more open-source and custom generators are used to create and share content online, many of which include no markings at all, systems that can identify AI-generated images or videos without relying on labels become necessary.

What this means for platforms 

Misinformation, CSAM, political deepfakes, claims fraud, violence, and other harmful content are already being generated with AI at a rapid pace. Bad actors can create this content and remove watermarks, making it appear real before spreading it online. This is where detection systems matter. They help prevent harmful AI-generated content from spreading and allow misuse or abuse to be flagged to the right teams for review and action.

Where this leaves us 

AI-generated content is now a normal part of the media landscape. For that reason, detection systems need to go beyond watermarks if platforms are going to meaningfully protect users, support online safety efforts, and enforce their policies consistently.

Detection is an ongoing process that requires regular training as new generators appear and existing models evolve. Systems must be able to continuously respond to changes in how content is produced.

If you want to see how this works across images, video, audio, and text, you can explore our AI content detection platforms and tools to better understand what is possible today. Visit our demo: https://hivedetect.ai/