Why Multi-Million Dollar AI Anti-Forgery Tech Can’t Defeat “MS Paint”?

When anti-counterfeiting mechanisms fail to bridge the analog hole, cracks appear in the standard of evidence.

Remember the early 2000s, when record giants like Sony poured millions of dollars into developing CD copy-protection technology? What happened next?

While software engineers worldwide agonized over complex cracks, a random netizen found a loophole: a cheap black Sharpie. By simply coloring the outer edge of the CD, a million-dollar defense system was instantly scrapped.

History has a funny way of repeating itself. Today, tech giants are using the same flawed logic, trying to solve copyright and deepfake crises by embedding “digital fingerprints” and “watermarks” into AI images. But this much-hyped defense line is embarrassingly fragile in the real world.

Here is how the three mainstream digital defense mechanisms are easily bypassed, one by one, using the lowest-tech tool imaginable: MS Paint.

ChatGPT (DALL-E 3)’s C2PA MetaData

When ChatGPT generates an image, it uses the C2PA (Content Credentials) standard to embed source data. It hides this digital fingerprint inside the file’s metadata. It is invisible to the naked eye but easily readable by specialized detection tools.

If you upload the image to a C2PA-supported platform like LinkedIn or open it with a scanner, the system will immediately display an “AI-Generated” tag or source credential.

Bypassing this is absurdly easy. Download the image, open it in the trusty old Windows MS Paint, hit “Save As,” and export it as a JPG. A couple of clicks later, this supposedly high-tech digital fingerprint evaporates completely.

No hacking is required. It is literally just the most basic form of file re-encoding.

Gemini’s Visible Watermark

Gemini uses a visible watermarking method, slapping an obvious AI-generated badge right in the corner of the picture.

This approach is straightforward and easy to grasp. However, bypassing it is so laughably easy that it barely warrants a discussion.

Once downloaded to your PC, just open it in MS Paint and crop the watermark out. You don’t even need a PC, use your phone’s native crop tool, and it’s gone in two seconds flat.

And just like that, a “watermarked AI image” transforms into a “clean, unmarked photo” in the blink of an eye.

Google SynthID’s Invisible Pixel Watermark

Google DeepMind’s SynthID is touted as the most cutting-edge, pixel-level invisible watermarking tech available. It bakes signals directly into the pixel distribution, which is completely invisible to the naked eye, and boasts high resilience against cropping, filters, and compression. Yet, if you subject it to heavy filtering, aggressive compression, or simply take a photo of the screen, that watermark still risks getting completely scrubbed.

Sounds tough to crack? Honestly, the internet crowd will always find the most primitive, dirt-cheap workaround imaginable.

Zero coding skills are required. Simply paste this “top-tier invisibly watermarked” image into a PowerPoint slide, go full screen, hit “PrintScreen” on your keyboard, and paste it right into MS Paint. This ancient move exploits the “Analog Hole,” physically severing every last digital fingerprint in a split second. Got a 4K monitor? Your screenshot might actually be crisper than the original AI file.

Feeling exceptionally lazy? Just toss the image into ChatGPT and tell it to draw a new one exactly like it. Just like that, every trace of the digital fingerprint is instantly laundered.

A Fatal Logical Fallacy: No Watermark Equals Original?

This brings us to the most crucial and deeply ironic question. When the public and social media platforms start relying solely on watermark detection as the standard for truth, it creates a highly dangerous reverse-misleading effect.

The perfect shield for bad actors: Since scrubbing AI traces is so effortless, anyone with genuine intent to forge images or spread deepfakes will absolutely wipe their files squeaky clean.

The absurd presumption of guilt versus innocence: A watermarked image gets slapped with an “AI” label and is instantly distrusted, even if someone just used AI to extend the background. Meanwhile, a watermark-free image, lacking digital evidence, is blindly trusted as a “human original” or “authentic photo.”

Ultimately, current AI fingerprinting mechanisms completely fail to catch actual malicious actors. Worse still, they end up endorsing “laundered” AI fakes, giving them a free pass to deceive the public while wearing the mask of pure originality.

Ego vs. Real Legal Consequences

Here lies a fatal blind spot most people completely ignore. Erasing a watermark is not just about protecting your ego; it is about assuming very real legal liability.

Many find it embarrassing to be caught using AI, so they casually crop or scrub the watermark away. But remember this: when you intentionally remove the AI tag to post a “clean” image, you are practically announcing to the world that you claim full ownership of that work.

If that AI-generated image happens to contain copyrighted elements (like a specific artist’s signature style, protected trademarks, or recognizable character traits), you can no longer hide behind the “AI made it” excuse.

By personally ripping up the platform’s disclaimer and proof of origin, you guarantee that 100% of the copyright infringement and legal fallout will land squarely on your own shoulders.

Conclusion

The ultimate absurdity of current AI watermarking is simple: it only keeps honest people out while doing absolutely nothing to stop the bad actors.

Anyone truly aiming to forge or spread deepfakes will absolutely scrub those watermarks clean. Meanwhile, ordinary users who casually wash away these tags expose themselves to massive, unnecessary risks. Ironically, these laundered AI images are then paraded around as “human originals” simply because their digital fingerprints are missing.

If images with actual physical pixels are this insanely easy to re-encode and rebuild, what hope is there for plain text, which is essentially just a string of Unicode characters?

When source attribution can no longer be reliably verified, we are no longer looking at a mere technical glitch. It becomes a fundamental crisis of evidentiary standards.

Next time, we will dive into the black-box witch hunt currently being waged against writers and students by so-called AI text detectors.

📌Author’s Note: This article was originally written in Chinese. To facilitate cross-linguistic knowledge exchange, I have collaborated with AI for translation and refinement. If this leads to the content being flagged as AI-Generated and affects your reading experience, please know that this was never my intention. My goal remains the clear and authentic sharing of insights across borders.