Date: 7/27/26 7:58 pm From: Tom Edell via groups.io <TEdell...> Subject: [slocobirding] FW: [CALBIRDS] artificial intelligence and photos of birds
I’ve heard from a few folks that they found the other post I forwarded of interest. This is a follow up CALBIRDS post. If you don’t take or process photos, you may want to hit delete.
Tom Edell
Cayucos, CA
From: <CALBIRDS...> <CALBIRDS...> On Behalf Of Jimmy Gain via groups.io
Sent: Sunday, July 26, 2026 8:12 PM
To: <z.querula...>
Cc: <CALBIRDS...>
Subject: Re: [CALBIRDS] artificial intelligence and photos of birds
A lot of bird photographers utilize denoise software to sharpen edges and especially eyes. I asked ChatGPT the following:
There is a lot of negative feedback regarding the use of generative AI in bird photos that are submitted to citizen science databases such as eBird and iNaturalist. There are concerns that the use of generative AI may mask or change important identification characteristics of a species of concern and lead to improper identification. Is the use of an application such as Topaz deNoise considered generative?
IT ANSWERED:
This is an excellent question, and one that is being actively debated among birders, photographers, and citizen science organizations.
The short answer is:
Topaz DeNoise AI (and its successor, Topaz Photo AI) uses AI, but it is generally not considered “generative AI” in the same sense as tools like Midjourney, Stable Diffusion, or Photoshop’s Generative Fill. However, the distinction is becoming less clear as AI-based photo editing evolves.
Here’s why.
Traditional denoising vs. generative AI
Traditional denoising algorithms work by mathematically reducing random sensor noise while attempting to preserve edges and detail.
AI denoising, such as Topaz DeNoise AI, is trained on millions of image pairs. Rather than simply smoothing pixels, it predicts what fine detail should look like based on what it has learned.
In doing so, it is not merely revealing information that already existed—it is making an educated prediction.
Is that “generative”?
Technically, yes—to a degree.
Computer vision researchers would describe many modern AI denoisers as generative or reconstructive neural networks because they synthesize plausible image content from learned models.
However, they differ from image generators because:
* they begin with an actual photograph,
* they preserve the scene geometry,
* they do not invent new birds or backgrounds,
* they are constrained by the existing image.
So most photographers think of Topaz as an AI restoration tool, not an image generator.
Why eBird reviewers worry
The concern isn’t whether the software is called “generative.”
The concern is whether important field marks may have been altered.
Examples include:
* faint wingbars becoming more prominent
* eye rings appearing cleaner than they were
* streaking becoming simplified
* bill shape subtly changing
* feather edges being “hallucinated”
* color transitions becoming smoother
For common species this rarely matters.
For a potential first county record, rare warbler, difficult gull, or hybrid, these changes could influence identification.
Where Topaz becomes more controversial
Recent versions of Topaz Photo AI include features like:
These functions are more likely to invent fine detail than simple noise reduction.
If a tiny bird occupies only a few dozen pixels, the software may create feather structure that never actually existed in the original capture.
That is what many reviewers are concerned about.
Current community consensus
Among experienced eBird reviewers and iNaturalist identifiers, the emerging consensus is something like this:
Generally acceptable
* Exposure adjustment
* White balance
* Contrast
* Cropping
* Traditional sharpening
* Moderate AI denoising
Use with caution
* AI sharpening
* AI detail recovery
* AI upscaling
* Super-resolution
Usually discouraged for documentation
* Generative Fill
* Object removal affecting the subject
* Adding missing features
* Replacing backgrounds
* Creating image content that was never captured
My recommendation for citizen science
If the photograph is being submitted primarily as documentation of a rare bird, I would:
* keep the edits conservative,
* avoid aggressive AI sharpening,
* avoid large AI upscales,
* retain the original RAW or JPEG,
* be prepared to provide the original if requested by a reviewer.
Many regional eBird reviewers already ask for original, unedited files when evaluating exceptional records.
Looking ahead
I suspect we’re moving toward a new vocabulary. Rather than asking whether an image is “AI” or “not AI,” reviewers may classify edits by how much they alter evidentiary content. An image used as scientific documentation should preserve the observable features present in the original capture as faithfully as possible.
As someone involved in bird photography and citizen science, you might appreciate a practical framework like this:
* Evidence-preserving edits: cropping, exposure, color balance, moderate noise reduction.
* Evidence-enhancing edits: AI denoise, AI sharpening, super-resolution—useful, but capable of altering subtle field marks.
* Evidence-generating edits: adding, removing, or fabricating image content; generally inappropriate for scientific documentation.
That framework focuses on the real issue—whether the edit changes the evidence—rather than on whether a particular tool is marketed as “generative AI.” It also provides a clearer basis for camera clubs and citizen science projects to develop consistent policies as AI-assisted editing continues to evolve.
Jim Gain
On Jul 26, 2026, at 10:23 AM, Ethan Monk via groups.io <z.querula...> <mailto:<z.querula...> > wrote:
Dear Birders,
Over the past year, several birders in California and further abroad have used artificial intelligence (“AI”) to edit photographs of birds and have uploaded those altered photos to eBird and iNaturalist. Even though most of these instances have probably been innocuous attempts to make a poor photo look a bit better, this practice is highly damaging regardless of a user's intended purpose. Unfortunately, most birders still do not understand how many of these programs work and why they are so negative. Below I have attempted to explain this issue.
There are several types of artificial intelligence, but the variety used by the most popular platforms (Google Gemini, Claude, etc.) is called generative AI. These programs have immense databases which they use to generate responses to user prompts. To put it simply, if you have a photo of a California Scrub-Jay, and you use a generative artificial intelligence program to edit the photo, the program is going outside of the data contained in the photo, searching its database for what it knows California Scrub-Jays look like, and then uses that external knowledge to “touch up” the photo to meet its expectations. This is not much different than printing a photo out onto paper and asking an artist to touch up the photo by painting onto it. The human artist will use their knowledge of what scrub-jays are supposed to look like to improve the image. So does AI.
To further clarify, I asked Google Gemini to explain how it edits photos. Here is what it told me: “Gemini is a Generator: It doesn’t actually “edit” your photo. Instead, it looks at your image, reads your instructions, and draws a brand-new picture from scratch based on patterns it knows. Because it rebuilds the entire image every time, it struggles to keep the original details—like the exact shape of a face, text, or background textures—identical.”
So, if you upload that Scrub-Jay, the generative AI can use its knowledge of scrub-jays to fill in missing details, and make that blurry photo sharp, or that dark spot visible, etc. But what if you upload a subpar photo of an Empidonax flycatcher? Or a seagull (yeah, I said it)? The AI still goes into its database and uses that external knowledge to touch up the photo… can the blurry Western Flycatcher be mistakenly touched up into a Willow? A Yellow-footed Gull into a Western Gull? Even if you upload a good photo, can the Sooty Fox Sparrow be made to look a bit more like a Slate-colored? Might a hatch-year state of molt return looking a bit more adult like? (If the number of remiges can change on a bird when run through AI, as I've seen a few times, it seems anything is possible.) How about a perfectly decent photo of an Epaulet Oriole, which when “touched up” with AI came out looking like the first-for-Brazil record of Red-winged Blackbird? More on that last scenario, and a further explanation of AI’s problems as they pertain to citizen science databases, here: https://www.nature.com/articles/s41559-026-03141-y
In short, generative AI should absolutely not be used when editing photos. These programs rely on pre-existing knowledge to edit photos, and in doing so, inevitably hallucinate details that were not actually there. At best, uploading these photos pollutes scientific databases with photos not representative of reality. (It is crazy that that is the "at best."]) And, I hate to say it, but with companies shoving AI down our collective throats, the onus is now on the user to verify that new photo editing apps and tools do not use generative artificial intelligence.
One final sobering note: Most of these instances where eBird reviewers or other careful eyed observers have sniffed out AI-altered media have done so because, to the trained eye, most AI edited photos often have the wrong number of feathers, a mishmash of features from different (sub)species, or otherwise appear "strange" or "off." But AI is improving rapidly. Just last year, I was unable to generate a photo of a bird in Google Gemini that did not seemingly combine features of multiple species and that was not positioned at an unnatural angle. Last week, I generated a photo of a Mourning Warbler with AI--completely from scratch, no pre-existing photo to serve as a "canvas" to work off of--that was scarily convincing. (I sent the photo to a few friends to ask what they thought-- the first to reply wrote, “Ok f*** this.”) Soon, we will not be able to tell what is and is not altered.
Please, build good habits now, and encourage the same from those around you by strongly advocating against the use of generative artificial intelligence to edit photos and by educating other birders of its perils.
