My laptop is one of my most important tools. While my servers and office systems have all of my configured software and documents, I can't take them with me on customer trips, or even to the living room for some late-night catching up while watching TV. The laptop is basically my portable window into my office.

I don't need a powerful laptop. I'm not a gamer and I rarely develop software directly on it. The biggest applications I use are Impress for presentations and VLC for watching movies on airplanes. Most of the time, I'm simply accessing websites, running diagnostics, or remotely logging into my office. (If someone were to steal my laptop, they won't get much. They can't access the office without my passwords and biometrics. If it ever were stolen, I can immediately lock down all network access to my office with a single unpublished URL.)

More Laptop

Last year, I wrote about my laptop. Back then, the Windows 10 operating system was hitting end-of-life and needed to be replaced. The final straw was the "Patch Tuesday" where the laptop sat at "Restarting" forever. Since the laptop is only used for remote access, there was nothing that needed keeping. I ended up reinstalling it with Ubuntu Linux.

This OS switch came with a few pleasant surprises:

• The laptop was significantly faster. (Windows is a resource hog!)


• The hard drive had a lot more room. (Linux is smaller than Windows.)


• Restoring the network from suspend worked perfectly. This had been a problem under Windows.


• The original battery lasted 8-9 hours under Windows, but had aged to lasting 4-6 hours from a full charge. With Linux, I was getting 10-12 hours of use from the same hardware.


• I had some touchpad issues under Windows. Switching to Linux made those issues mostly go away.

Touchpad

While the touchpad issues were mostly resolved, they weren't completely gone. The mouse cursor would move correctly, but sometimes the mouse buttons would become non-responsive or require multiple presses before they worked. A reboot would fix the problem temporarily, so I didn't think it was the hardware wearing out.

Under Windows, I found a few other people with the same problem, but "reboot Windows" was the cure for everything. With Linux, there are enough tools for a real diagnosis and easy fix.

The laptop communicates with the touchpad using a two-wire protocol called I2C. When the laptop suspends and restores, the I2C drivers can get into an inconsistent state, causing the buttons to fail. The solution? I created a system restore script that restarts the I2C drivers when it wakes up. With Ubuntu, create the executable file "/lib/systemd/system-sleep/restart-touchpad":

#!/bin/sh

case "$1" in

    post)

        # Unload the ACPI and I2C drivers

        rmmod i2c_hid_acpi

        rmmod i2c_hid



        # Reload them for a clean reset

        modprobe i2c_hid

        modprobe i2c_hid_acpi

        ;;

esac

That's it. You don't need to restart anything. Now the touchpad works properly all of the time.

Old Hardware

I usually keep hardware until it stops working. For example, I had an old Pentium computer with a 120MB hard drive that I used as my mail server for over 20 years. OS patches? Ha! It was still running Redhat 5.1! (Old hacker security tip: nobody looks for 20-year-old vulnerabilities, and newer vulnerabilities didn't work on old systems.) In my opinion, as long as the system is stable, why risk replacing it? The only reason I retired that old mail server was that everyone was moving to TLS for secure email transfers and some of the OpenSSL dependencies were too complicated to port to the old system. The 30+ year old hardware itself still worked fine.

The same goes with laptops. For someone who has been in the computer field for over 40 years, I've only ever owned four laptops. My first one was an Apple. (Never again.) It lost OS support after 2 years. However, I didn't move off of the laptop until the browser providers (Chrome and Firefox) stopped supporting it. I needed a modern browser, so that meant a modern laptop.

My Asus EeePC was my favorite because it was tiny and lightweight. However, after a decade most OS's dropped support for the Atom processor, so I had to update again.

These days, I'm using a Dell XPS that I purchased in 2017. The hardware is designed to last, the only issue was the touchpad -- and that's fixed now. That just left the battery.

Battery

No laptop batteries last forever. With lithium-ion, they start with a long lifespan and then slowly degrade over time. However "slowly" isn't linear. After a few years, you'll start seeing the battery runtime decline, and as time passes it will decline very rapidly. When it's completely dead, it might hold a charge for five minutes.

Lithium-ion batteries typically last seven to ten years, although heavy cycling, deep discharges, and heat can shorten that lifespan. My laptop was from 2017, putting it well into the "old battery" range. When it was new, it could hold a charge for 8-10 hours while running Windows. After eight years, Windows was lasting 4-6 hours before I switched to Linux. Linux lightened the power requirements, bringing it back to a 10 hour battery. That doesn't mean that the battery is fresh; it just means that the new OS was more power efficient.

Over the last year, it had entered the fast decline that is typical for lithium batteries. Last month, a full charge was lasting 2.5 - 3.5 hours (still under Linux). If I can't last an entire airplane flight, then that's effectively a dead battery. While I'm thrilled to have had nearly 9.5 years out of this battery, I had to make a choice:

• (A) Get a new laptop. These days, that would cost me $900 - $1500.


• (B) Get a new battery. I saw prices that varied from $25 to $100.

My thought: if I could get a new battery and maybe another 7 years of life out of this laptop, then it was definitely worth it. (Also, I wouldn't have to lose all of the stickers I had plastered on the laptop.) In the worst case, either a new battery wouldn't help or I'd damage the laptop while swapping batteries. But for under $100? I was willing to experiment.

The huge price range really bothered me. As far as I can tell, all of the sub-$75 batteries were from pop-up providers. Vendors who were here today and gone tomorrow. Each had reviews that ranged from "5 stars: It works!" to "1 star" with long paragraphs about all of the problems and non-responsive vendors. Even though the batteries were all marketed as "new", they were probably "newly rebuilt" and not really "new".

Dell no longer makes this battery, but a few companies still have good reputations for selling genuinely new batteries rather than rebuilt packs. I had never purchased from iFixIt before. And now that I have, I highly recommend them. (I am not a paid spokesperson, I'm just a very happy customer. As an aside, I often blog about problems with vendors. This time, I only have positive things to say.)

The battery was affordable (under $100 and with free shipping). It arrived on time. It arrived well-packaged and undamaged. (This is always a concern with lithium batteries.) It said that the package included a small toolbox, but that wasn't part of my decision process. Now that I've used it, this is one of the nicest toolboxes I've ever had for repairing equipment. It includes:

• A metal shim, for prying open lids without cracking the tabs.


• A bunch of plastic shims, so the lid doesn't close while sliding the metal shim around the seams.


• Every screw driver bit size you might need.


• High quality tweezers.


• A plastic tool that is great for helping peel up tape.


• A suction cup, in case you're repairing a cellphone screen.


• Even the toolbox lid is well-designed, with grids for holding screws. I populated it with the screws I pulled out (each type and location went in a different holder) and put the screwdriver bit next to it as I went.

I found out the hard way that it is designed for use with one hand! With my laptop, I had removed the screws and was using the metal shim to pry off the back. I realized that I needed a plastic shim to prevent the lid from snapping back on while I worked. With one hand, I held the metal shim in the lid. With the other hand, I was able to push down on one side of the plastic shim and have it pop up so I could grab it. I didn't realize that there were multiple plastic shims until they popped up and exploded all over the desk. This was a very pleasant surprise.

Here's the toolbox:

(Be careful pushing on the blue plastic triangular shims in the middle. There are a bunch of them and they will all suddenly pop out!)

And here's the laptop mid-replacement:

(The old battery is off the top of the photo. The new battery is the black rectangle in the top center. It goes over the touchpad, which is the green board at the bottom of the screen.)

A few years ago, I tried to replace the battery in my Samsung tablet. I had a hodgepodge collection of tools and ended up destroying the tablet. I was worried about doing the same thing to the laptop. But with the right tools, going slowly, and taking photos, I managed to replace the battery without any problems in under 30 minutes. (Now that I know what I'm doing, I could probably do it in 10 minutes without feeling rushed.) It was truly painless.

Calibration

After swapping the battery, the laptop wouldn't turn on. That's fine -- the new battery shipped without a charge. After 10 minutes of charging, I could turn the laptop on. (Good! I didn't break anything.)

The next step is to calibrate the battery. This isn't for the battery's health; it calibrates the software that reports how much power remains.

• Typical batteries: With regular lead-acid and alkaline batteries, the output voltage is pretty linear. You can measure the voltage to determine the battery's remaining capacity.


• Lithium-ion batteries: Lithium-ion batteries have a long, flat discharge rate. You can't just look at the voltage and determine how much battery time is left. During the flat discharge rate, the micro-voltage differences can be too small for the hardware to detect; there may be no measurable difference between 30% and 70% capacity. To estimate the remaining time, the OS uses a combination of measured voltage and a timer for how long it took to drain. For the calibration, you put it through a full charging cycle, full discharge, and full charge again. This helps the software guestimate the capacity during the flat discharge rate.

My laptop's battery is rated at 7.6V and 60Wh. With Windows, I was getting nearly 10 hours with the original battery. But now I'm on Linux, which consumes much less power. I had no idea how long this new battery would last.

To calibrate with Linux, you should remove the power history. This forces it to learn based on the new battery.

sudo systemctl stop upower
sudo rm /var/lib/upower/history-*
sudo systemctl start upower

Next, fully charge the battery. Let it charge for at least two hours beyond "fully charged".

The full discharge step is kind of a challenge, since the OS wants to be as efficient as possible. Turn off power-saving mode, turn off the screen saver, turn off suspend, etc. I gave it something to do: play the movie "The Bourne Identity" over and over until the battery was fully drained. (The computer will warn about low voltage, and then shutdown automatically. That's the full discharge.) With this new battery? I expected it to last for 12 or 14 hours. Instead, it ran for 30 hours! (I suspect that it was using a hardware-based video decoder which is very power-efficient, and letting the CPU itself effectively rest and consume flea power.)

The final full charge cycle probably only needed a few hours, but I let it go overnight.

With the new battery installed and calibrated, Linux reports "15 hours remaining". After five hours of use, I still have "14 hours remaining", and if I start compiling code, it drops to "10 hours remaining." I'm not too worried about the calibration's accuracy since it may take the OS time to learn. I might not be able to tell the time remaining with extreme accuracy, but I'm sure it will last an entire plane flight on travel days.

Right to Repair

There's an entire movement centered around the right to repair equipment. Being able to change a battery in a working device is one of those basic tenets. My Samsung tablet appeared designed to self-destruct when opened. My Dell laptop was built to be repaired, and the tools from iFixIt simplified this process. I'm sure my laptop is good for at least another seven years.

In my home town of Fort Collins, they are building a new library. This one will include an "innovation maker-space". They recently had an open house to discuss wants and needs with the community. People wanted everything from a 3D printer and laser cutter to sewing machines, button makers, and classes on gardening. One of the things that was repeatedly mentioned by attendees was a repair station. Whether it's a team of volunteers, one-time hands-on classes, or something in between, people have a strong desire to repair electronics before buying a replacement.

My successful weekend project is exactly why community maker-spaces are so vital. When manufacturers design hardware to be opened, and companies like iFixIt provide the exact toolkits to do it safely, fixing our own tech transitions from a stressful gamble to a rewarding afternoon project. We don't need to throw away perfectly good silicon just because a battery gets old. Keeping this laptop out of a landfill isn't just a win for my wallet, it's a small victory for a more sustainable, fix-it-first mindset.

The combination of a lightweight Linux OS, a fresh battery, and the right tools completely resurrected a piece of hardware that most people would have recycled years ago. It may not be the latest or greatest, but it's perfect for the next time I leave the office.

I'm wrapping up my investigation into invisible watermark algorithms and I am extremely disappointed. Not only do none of the modern AI-based algorithms work as they claim, it turns out that they are all making the same fundamental mistake.

I previously evaluated Google's SynthID and Adobe's TrustMark algorithms. Both of them claim to have incredibly accurate results.

• According to Google's peer-reviewed and published paper, they claim to have a true positive rate (TPR) above 99.97% -- meaning that they will miss their own watermarks less than 1 in 10,000 times. However, my own empirical testing found that is it much closer to 1 in 20. Moreover, SynthID is proprietary and only accessible through Google's "Gemini" AI system. Gemini has been observed hallucinating results and providing contradictory conclusions depending on how the question is phrased.


• According to Adobe's Content Authenticity Initiative, their TrustMark "can exceed 96% bit accuracy at around 42-45dB PSNR quality under severe noise degradations". However, that statistic focuses on resilience and not accuracy. In my empirical tests, I found that TrustMark has a 10%-20% false positive rate, effectively making it useless. (If you see a TrustMark signature, then it is very likely random noise and not an actual signature.)

This time, I evaluated Meta's "Stable Signature" algorithm. (Their paper and code are in GitHub.) This system encodes a 48-bit sequence into the picture's visual content. The idea is that you can encode a unique 48-bit sequence as your watermark. If your decoder finds the same 48-bit sequence, then it can identify your own watermark.

WARNING: This blog entry leans heavily into math and statistics to prove that Stable Signature, TrustMark, and SynthID are nowhere near as reliable as their developers claim.

The Basic Algorithm

Traditional (non-AI) invisible watermarks typically hide in subtle locations, such as the least significant bits, changes in brightness (e.g., Digimarc) or the frequency spectrum (DCT or FFT). There is always the risk that image encoding could corrupt the hidden data, so these algorithms typically rely on repetition over the image to help identify the true signal. In addition, they may include error correction code (extra bits in the data) to fix any minor data errors.

However, there is a problem with the traditional approaches: injecting hidden data in the image could create visible distortions. The modern approach uses an AI system to better hide the data with less added distortion.

As with SynthID and TrustMark, Stable Signature encodes binary data and uses an AI-model to decide where to hide it in the image. The AI is tuned to minimize visible distortions when embedding the data. Later, an AI-based decoder looks at the image and identifies the likely location where bits are stored, then it extracts the data.

There is always the case that the data may be mixed with noise. Different AI-based watermarking systems rely on different techniques for reducing the noise. For example:

• Google's SynthID only stores a few bits of data (effectively a flag or version number). This allows them to use a lot of data as repetition and to increase the accuracy rate.


• Adobe's TrustMark uses the Bose-Chaudhuri-Hocquenghem (BCH) algorithm. This acts as a combination of checksum and error correcting code that should reduce the number of errors.

Meta's Stable Signature uses a simple Hamming distance.



The Hamming distance measures the number of bits that need to be swapped in order to correct the code. In effect, it defines a set of stable states (e.g., 10110 and 11000) and places a ring around each state that represents the single bit changes. If you change enough bits, then you will reach a different stable state.

According to Meta's Stable Signature research paper, the 48-bits should be uniformly distributed and cites a "false positive rate below 10^-6", or 1 in one million. This means you can choose a 48-bit sequence to use as your signature. Every picture will generate a 48-bit sequence, and the sequence can vary a little based on noise in the picture. However, if you find a code that is within a short Hamming distance of your code (e.g., within 6 bits difference), then you can determine that it is the same code with a high reliability.

At least, that's the theory.

Empirical Testing

I went into this experiment assuming that everything works like they claim. I want to be able to reliably identify invisible watermarks associated with Meta. What I don't know is what sequence they use, or whether they use multiple codes depending on whether it comes from Meta's AI system, Facebook, Instagram, WhatsApp, etc.

Fortunately, this is something I can test! I grabbed an uncurated sample of pictures from FotoForensics: the first 10,000 unique images uploaded last month (May 2026). If the bit sequences are uniformly distributed with a "1 in 1 million" collision rate, then I should see a huge number of unique bit sequences and a few small clusters around pictures from Meta (Meta AI, Facebook, Instagram, etc.). Those clusters will represent the invisible watermarks used by Meta.

The results from my empirical test were definitely not what I expected. I found:

• No clusters associated with any Meta images. This suggests that Meta does not use their own Stable Signature watermarking software found on GitHub.


• With a random distribution, there should be no clusters. However, I had 25 different pictures that had the exact same bit sequence: 110110100111111011101001111000100111011000011101. With a 1 in a million collision rate, this should not happen! These pictures came from very different sources. Here's four of the 25 pictures (ranging from planets to light bulbs to text with a transparent (black) background):
  
  
  
  All of these pictures have dark/black backgrounds and something bright in the middle. This suggests that Stable Signature operates more like a perceptual hash than an invisible watermark.


• Stable Signature uses a Hamming distance to identify a cluster. If I assume the 25 pictures are the center (centroid) of the cluster and use a 6-bit Hamming distance, then there are 356 pictures that are similar. And if I assume that the 25 pictures are not the center but part of a cluster, then a Hamming distance of 6 has a cluster of 450 pictures centered 3 bits away, at 110110000111111011101011111000100111001000011101. This cluster represents 4.5% of the uncurated image data set! Here are a few samples from this larger cluster:
  
  
  
  (I'm explicitly not sharing pictures with personal information, like invoices, recognizable people, and GPS information.)

It's not just one random cluster that is massively large (450 pictures out of 10,000). There's a cluster of 184 pictures at 110101001011001011001011111000100111001000011101, 58 pictures at 110100000011111010001001111000100111011000011101, etc. I found over 60 clusters with more than 10 pictures each at a Hamming distance of 6. That should not happen with a "1 in 1 million" collision rate.

Independent Analysis

I went back to Meta's research paper to see if I could find the discrepancy. And there it was, in section 3.1: They tested their system against the hypothesis that the 48-bits are each independent and uniformly distributed. The problem is, they use one neural network to generate the bits. That explicitly means that the bits are dependent, not independent.

Their paper assumes a binomial distribution. That is, given an arbitrary image, the 48-bits represent a random coin flip. The math becomes:
P(X ≤ T)=∑Tk=0(48k)(0.5)k(0.5)48−k

This computes the probability of 48 random bits being within a Hamming distance (T). The probabilities table becomes:

Hamming Distance Threshold (T)	Bit Error Rate (BER)	Probability of a Random Image Matching by Chance
14 bits or fewer	≤ 29.17%	1 in 362.63
13 bits or fewer	≤ 27.08%	1 in 957.81
12 bits or fewer	≤ 25.00%	1 in 2,788.35
11 bits or fewer	≤ 22.92%	1 in 8,999.08
10 bits or fewer	≤ 20.83%	1 in 32,416.80
9 bits or fewer	≤ 18.75%	1 in 131,390.28
8 bits or fewer	≤ 16.67%	1 in 605,094.89
7 bits or fewer	≤ 14.58%	1 in 3.20 Million
6 bits or fewer	≤ 12.50%	1 in 19.83 Million
5 bits or fewer	≤ 10.42%	1 in 146.19 Million
4 bits or fewer	≤ 8.33%	1 in 1.32 Billion
3 bits or fewer	≤ 6.25%	1 in 15.24 Billion
2 bits or fewer	≤ 4.17%	1 in 239.15 Billion
1 bit or fewer	≤ 2.08%	1 in 5.74 Trillion
0 bits (perfect match)	= 0.00%	1 in 281.47 Trillion

Meta's paper says that they use a Hamming distance of 7 bits (requiring 41 of 48 bits), which matches their claim of a "false positive rate below 10⁻⁶". However, I'm seeing problems at a Hamming distance of 6 (should be 1 in 20 million) and even collisions at 0 (1 in 281 trillion)!

The Core Problem

There is clearly a discrepancy between the theoretical probabilities and the empirical testing. When I looked back over Meta's research paper, I saw the problem:

According to Meta's paper, each of the 48-bits are independent. In a perfectly independent 48-bit hypercube, un-watermarked images should scatter uniformly across all 2⁴⁸ possible values. However, neural networks map a non-linear manifold (a multi-dimensional wavy surface) through this hypercube. This mathematical landscape is warped with its own peaks, ravines, and valleys. It has attractors that form clusters, and repulsers that form voids where stable values can never exist; this is a feature of a neural network. And most importantly, the output bits are explicitly not independent.



The left diagram illustrates an expected uniform distribution if all of the bits were independent. The right diagram are the types of theoretical clusters that form when the bits are dependent. There should be clusters around attractors and voids (areas with no dots) from the repelling regions.

Moving from theoretical to empirical, I graphed the data. The 48 bits can be represented as bytes. I took the first 24 bits and converted them into 8-bit red, green, and blue pixel colors. If the data is truly random, then the colored dots should be distributed across the RGB cube. However, if the bits are dependent, then there should be very clear clusters, structures, and voids. Here's the graph:



Yes, there are very clear structures that look like planes and lines. Within the planes are clusters, and outside the planes are very large voids -- areas where there are no dots at all. The data generated by Meta's Stable Signature implementation fails this basic test for independence.

The biggest cluster that I found represents a Zero Signal Bias (ZSB). When their neural network doesn't find a watermark, it moves the 48 bits toward a strong attractor, like a massive gravitational well. At 6 bits error, it should have a collision of around 1 in 20 Million. But in reality, my 10,000 pictures had a cluster of 450 images within 6 bits due to the ZSB. That's an error rate of around 1 in 22 with the ZSB alone. If we add in all of the other clusters that contain at least 10 pictures, then 2327 pictures are in various clusters; we're looking at an error rate around 1 in 4 -- and that's at a Hamming distance of 6, which is more conservative than their paper's Hamming distance of 7. (In AI terms, this is a representation collapse or structural bias that is typical for deep neural networks.)

(As an aside: Given their "1 in 1 million" claim, I could look for any clusters of 2 or more pictures. At clusters of 2 or larger, 5,237 of the 10,000 test images were in clusters, or 52%. If you show their algorithm 10,000 pictures, then there is a better-than 50% chance of a false positive match.)

Less Than Random

It's one thing for me to claim that there are visible clusters and to show pictures of clusters, but another to prove it mathematically. (Time to dust off my college textbooks from "Introduction to Statistics"...)

I fed Meta's code the first 10,000 images from May 2026. A few of the images were in unsupported formats (HEIC, WebP, and a few corrupted JPEG files), resulting in 9,847 viable pictures. I evaluated this data with elements from the NIST Statistical Test Suite (SP 800-22) for randomness, including a monobit test and Chi-Squared (χ²) test for independence.

The monobit test determines if the baseline frequency of adjacent bits seems independent.

• Total Bits Processed: 9,847 pictures × 48 bits per signature = 472,656 bits
• Observed Count of Ones ('1'): 266,419
• Observed Count of Zeros ('0'): 206,237
• Expected Count (E): 236,328 for each.

Running a simple standard Chi-Square Goodness-of-Fit test for this bit balance:
χ2=(266419 − 236328)2236328+(206237 − 236328)2236328= 3816.14 + 3816.14 = 7632.28

• In mathemat-ese: with 1 degree of freedom, a χ² statistic of 7,632.28 yields a p-value infinitely close to 0.0 (p ⋘ 10^-100). (As an aside, most Chi-square tables usually evaluate the 1 degree of freedom up to around χ²=10. This χ² value is so astronomically high that the probability p effectively becomes zero.)


• In English: That's definitely not random or independent.

The watermark extraction is strongly biased toward producing 1s over 0s across global arbitrary images (roughly 56% ones to 44% zeros). This immediately violates the uniform distribution assumption.

The second test is the Chi-Square (χ²) Test for Serial Independence. If the bits were independent, the transition probability between adjacent bits would just be the product of their individual probabilities. This table shows the occurrence rate of the transition pairs across all of the observed 10,000 (well, 9,847) pictures:

Transition Pair	Observed Count (O)	Expected Count under Independence (E)
0 to 0	106,750	90,051
0 to 1	95,296	116,186
1 to 0	95,302	116,186
1 to 1	165,461	149,976

χ2=∑(O − E)2Eχ2=16699290051+(−20890)2116186+(−20884)2116186+154852149976=3096.7 + 3756.2 + 3754.0 + 1599.0=12,205.9

• In mathemat-ese: With 1 degree of freedom for the transition contingency table (accounting for fixed margins), a χ² value of 12,205.9 gives a p-value of 0.0.


• In English: Ain't no way this is random or independent.

And as if this wasn't conclusive enough, there are other tests we could apply:

• Static Tail Patterns: Looking closely at the end of the 48-bit sequences, a massive cluster of strings end explicitly in ...111101 or ...00111101. Additionally, bit position 46 is nearly always "1" (228 zeros vs 9619 ones, or 97.7% of the time it is "1"), position 47 is "0" (8958 of 9847 images, or 90.97%), and position 48 is "1" (found with 9696 images, or 98.5%) across thousands of uncurated, real-world images.


• Structural Clustering: Certain bit columns share an extraordinarily high Mutual Information score (I(X;Y)). For example, knowing the output of bit position 12 gives you better than an 80% accuracy in predicting bit position 28.

The assumption of a "uniform distribution over arbitrary pictures" relies on the idealistic premise that random natural image features project uniformly across the decision boundaries of a network. However, because the extraction network maps inputs to a constrained, highly continuous hyper-dimensional manifold, the network's latent layers natively enforce structural smoothness.

For the TL;DR crowd:
Meta's researchers made a fundamental mistake when computing their accuracy rates. It's not a "1 in 1 million" chance of a false match, it's closer to 1 in 4 -- because the 48 bit values per signature are not independent.

As I re-read Meta's research paper, I realized that the statistical error wasn't an oversight; Meta's researchers explicitly acknowledged the problem. In their paper (Section 4.1), they wrote:

Second, we observed that W’s output bits for vanilla images are correlated and highly biased, which violates the assumptions of Sec. 3.1 [the section about independent statistical test methods].

In other words, they recognized that the extracted bits are not independent. Despite this, their published false-positive analysis still relies on the assumption that the bits are independent.

Widespread Problems

Knowing that Meta's accuracy rate is grossly inflated due to assuming bit-wise independence when there is none, I looked back over Google's and Adobe's papers for their own watermarks. Did Google's and Adobe's researchers make this same mistake?

• Google's SynthID research paper talks in terms of True Positive Rates (TPR). They do make this same "bit-wise independent" mistake, but it's obfuscated in the paper. You can see the error in their Equation 3 (PDF page 8), where they assume there is a uniform (independent) distribution. Their paper hyperfocuses on the true positive rate and never addresses the false positive distribution. (Either they didn't know to look, or they knew and decided to not report it because it would expose a serious weakness in their solution.)


• Adobe's TrustMark research paper also makes assumptions of independence. You can see this in their PDF with the binary cross-entropy loss in Section 3.1.4. This mathematically treats each bit position as an independent Bernoulli trial. (By definition, a Bernoulli process strictly requires independence.) In their experiments (Section 4.1), they wrote "At test time, every image is associated with a random watermark", but they never tested if the random watermarks were similar to each other.

This introduction-to-statistics mistake is found in all three of these invisible watermarking technologies. The detections produced by these systems are so unreliable that an analyst cannot determine whether a reported detection is real or a false positive, or whether a reported non-detection is genuine or a false negative.

It's also worth noting that, shortly after releasing Stable Signature, Meta developed another algorithm: Pixel Seal. (Not to be confused with my own Secure Evidence Attribution Label / SEAL technology.) Pixel Seal moves to a 256-bit payload to increase the capacity, and their related model, Chunky Seal, pushes up to 1024 bits. While Meta's approach focuses heavily on addressing the invisibility side using an adversarial-only discriminator, the underlying approach still uses a neural network mapping. Using more bits only exacerbates this flaw.

Potential Uses

Algorithms can have uses. For example, Meta, Google, and Adobe are training their own AI models on images that they encounter. To prevent poisoning their training sets, they want to exclude images generated by their own systems. In this regard, watermarking does help them. For example, if Meta excludes an extra 25% of images (from false positives), then they still have a lot of images that they can train on.

However, that same usage does not work with legal cases. For example, consider an insurance company. Most insurance claims today include photographic evidence. The company wants camera-original photos, but have to use whatever the customer submits. The problem is that there is a lot of insurance fraud. In theory, seeing a watermark from an AI system like Meta, Google, or Adobe, should be great for identifying and ruling out fraud. Unfortunately, Stable Signature, SynthID, and TrustMark are so inaccurate that none of them can be trusted; it's not even worth testing to see if customer photos contain these invisible watermarks.

For these watermarking systems, I'm talking about very high error rates: roughly 1-in-4 for Meta, 1-in-5 for Adobe, and 1-in-20 for Google. But let's pretend that they work much better, like a 1-in-20,000 false positive rate. An insurer processing 100,000 claims per month would expect to accuse around 5 completely honest customers of fraud each month. Falsely denying 5 out of 100,000 claims? That creates a toxic customer service nightmare, severe legal liability, and fines from regulatory bodies for bad-faith claim denials. This could even become a class-action lawsuit that they couldn't win.

As bad as it is for insurance and financial institutions, there are much higher stakes at play. The EU AI Act (Article 50(2)), China's GB 45438-2025, California SB 942, and similar legislation are moving toward mandating AI content watermarking.

The failure of these three leading systems, from three Fortune-500 companies, to meet their own claimed accuracy rates is not just an academic curiosity. Regulators and courts will employ these systems for attribution and fraud detection. Reliable AI-based watermarking technology is not ready.

Three companies. Three algorithms. Three different research teams. The same fundamental error. The false positives won't go on trial. People will.