remove-ai-watermarks/docs/known-limitations.md

# Known limitations

This page describes current product limits. Historical measurements and
superseded experiments live in the research archive listed in
[the documentation index](index.md).

## Visible removal

### Fill quality depends on the background

Visible removal changes only the selected mask, but the hidden pixels still
have to be reconstructed.

- OpenCV is fast and requires no model download. It works well on flat
  backgrounds but
  can smear texture or repeated structure.
- MI-GAN is a lighter learned backend. It can improve natural texture but may
  ghost or invent structure.
- LaMa is the heaviest learned backend and is generally the strongest option
  for difficult backgrounds.

`--backend auto` selects LaMa when available, then MI-GAN, then OpenCV.

No backend can recover detail that is completely hidden by an opaque mark. A
successful detection therefore does not guarantee a visually perfect fill.

### Automatic detection covers registered variants only

The registry contains vendor and locale specific templates. A redesigned mark,
an unsupported locale, a different position, or a crop may be missed.

Known examples:

- Samsung detection is calibrated for the Italian
  `Contenuti generati dall'AI` text variant.
- The Jimeng top-left pill has a weak visual detector and is intentionally
  subject to additional product and background checks.
- Kling support covers the calibrated variants rather than every Kling label.

Use `erase --region` when you can see and select an unsupported or missed mark.

### Strict and automatic sensitivity trade recall for precision

`--sensitivity strict` uses the visual gate alone. The default `auto` mode may
relax a mark only when metadata or a confidently detected sibling mark
corroborates the same product.

There is no blanket "this image is AI" relaxation. That information does not
identify the vendor, mark, or location and caused unacceptable false
detections in the removed experimental mode.

## Invisible removal

### Regeneration is lossy

Invisible removal does not decode and delete a payload. It regenerates the
image through a diffusion pipeline. Faces, text, colors, and fine detail can
change even when the watermark is successfully disrupted.

ControlNet is the default compatibility profile. It conditions on edges to
preserve structure, but edges do not preserve identity or exact texture.

The CUDA only `qwen-zimage` profile adds a separate face stage and is the
highest fidelity option in the current implementation. It is larger, slower,
and still may alter small text or difficult faces.

### Removal cannot be verified locally for proprietary SynthID

The project has no public local SynthID pixel decoder. It can infer likely
presence from supported provenance metadata, but a missing metadata proxy is
not a negative pixel verdict.

For important outputs:

1. preserve the original;
2. process a copy;
3. verify with the matching provider tool when available;
4. do not assume one provider's verifier covers another provider's payload.

Provider systems can change, so a result verified on one file, seed, or version
is not a permanent certification.

### Video SynthID removal is lossy and content-dependent

The `video invisible` command and `remove_video_invisible` API regenerate video
pixels through a VAE. The shipped `noise_std=0.15` profile is oracle-certified,
but Google does not publish a local decoder for arbitrary runtime outputs. A
quiet metadata scan, paired PSNR, and the temporal-residual metric are fidelity
measurements, not independent SynthID verdicts.

The control must use the same clip, frame rate, dimensions, and final codec as
the candidates. The separate `scripts/video_synthid_sweep.py` harness produces
that matched control. If the control is not detected by the matching provider
oracle, the experiment cannot attribute a quiet candidate to regeneration.

The 2026-07-29 two-clip calibration used Gemini's built-in content verifier:
both matched controls were SynthID-positive, the stronger candidate was
negative on both carriers, and a weaker candidate was negative on one. A
2026-07-30 adversarial follow-up incorrectly asked the ordinary chat model to
reinterpret the verifier while excluding every other input; its `UNAVAILABLE`
answer was not another detector run and does not invalidate the original
built-in results. The calibrated default remains content-dependent; a fresh
source-positive, output-negative pair is an optional audit for unusually
important files or after provider changes.

The 2026-07-31 full-clip check used the public eight-second Veo off-road sample.
The source was detected across the full clip, the complete product path at
`noise_std=0.10` remained detected, and `0.15` returned no SynthID detection.
The default was raised to `0.15`. At 512 px / 12 fps, the accepted candidate
measured 25.39 dB paired PSNR and a 1.058 motion-compensated temporal-residual
ratio. This is one carrier, not a universal guarantee; hashes and exact verdicts
are tracked in `data/evaluations/video-synthid-oracle.csv`.

The shipped engine streams sampled frames in bounded batches, computes its
fidelity metrics incrementally, and pipes regenerated pixels directly to
ffmpeg. Its frame and latent memory is therefore bounded by `--batch-size`
rather than clip duration. Runtime still grows linearly with duration, and the
separate multi-candidate research sweep deliberately retains its short sampled
prefix so it can reuse identical latents across candidate strengths.

### Strength is content and seed dependent

The two profiles resolve an unset strength differently, because different things
were measured for each.

`qwen-zimage` reads it from image area, through the resolution-adaptive denoise
curve. The vendor is deliberately ignored: the curve, not the issuer, is what was
calibrated.

`sdxl-zimage` reads it from the C2PA issuer, on a flat ladder:

- OpenAI: `0.15`;
- Google: `0.25`;
- unknown: `0.25`, following the stricter of the two.

An SDXL global pass needs more denoise than Qwen at the same fidelity, and the
values are flat rather than a curve because flat values are what was measured: each
verdict came from a fixed strength at one size, and no size dependence has been
established for that stage.

An explicit `--strength` overrides both. The defaults are operating points, not
universal guarantees. Near a removal threshold, different content or a different
random seed may change the verifier result, which is why both profiles are
certified at a fixed seed. The live resolver is
[`watermark_profiles.py`](../src/remove_ai_watermarks/_internal/watermark_profiles.py).

### Pipelines have different quality tradeoffs

| Pipeline | Main limit |
| --- | --- |
| `qwen-zimage` | CUDA only, large model stack, and limited broad certification across seeds and content. |
| `sdxl-zimage` | CUDA only. Its strength ladder is flat per vendor, not a resolution curve, because flat values are what was measured. |

The `controlnet`, `sdxl`, `qwen` and `default` profiles were removed, not aliased
onward: a retired name is rejected at parse time rather than routed into a profile
the caller never chose. There is no `--model`, `--steps`, `--guidance-scale`,
`--device` or `--auto` option either; each profile pins its model stack, its
per-stage schedule, CFG 1.0 and CUDA.

## Resolution and memory

### Small images are processed at their native size

There is no minimum-resolution floor. It existed to enlarge small inputs toward
SDXL's ~1024 training resolution and was removed with the SDXL profiles, which
never applied it anyway. Both surviving profiles run at native geometry, so a
small input is neither enlarged before diffusion nor restored afterward.

`--max-resolution` still caps very large inputs, and only ever scales down.

### Large images stay at native resolution unless capped

`--max-resolution 0` means no explicit downscale cap. A positive value caps the
long side before diffusion and restores the result afterward. This reduces
memory use but introduces a downscale and upscale round trip.

`--tile` preserves the input dimensions while running the diffusion stage in
overlapping tiles. It avoids the explicit downscale, but it is not pixel
lossless: each tile is independently regenerated. With `qwen-zimage`, only the
global Qwen stage is tiled; the face stage runs after tile blending.

### CPU offload is CUDA only

`--cpu-offload` moves Diffusers model components between CPU and CUDA instead of
keeping the complete standard pipeline in GPU memory. For `qwen-zimage`, it
forces the face stack to use its offload path.

The option reduces CUDA memory pressure at the cost of speed. It has no effect
on CPU or MPS and fails loudly when a CUDA Diffusers pipeline does not expose
the required offload method.

### MPS may fall back to CPU

The SDXL paths include an MPS out-of-memory fallback that reloads on CPU. A run
that appears much slower after an MPS failure may be continuing on CPU.

Memory needs depend on the pipeline, input size, dtype, and machine. Use tiling,
a resolution cap, or a lighter pipeline when necessary.

## Metadata and formats

### Missing metadata does not mean clean

Screenshots, social platforms, and re-encoding can remove metadata while a
pixel watermark remains. `identify` therefore reports unknown rather than
clean when no supported signal is found.

### JPEG XL is metadata only

The metadata path recognizes JPEG XL containers, but the visible and diffusion
image paths do not list `.jxl` as a supported pixel format because the package
does not include a JPEG XL pixel decoder.

### HEIC, HEIF, and AVIF pixel decoding uses an optional Pillow fallback

OpenCV does not decode these formats in the project. `image_io.imread` falls
back to Pillow with `pillow-heif` when the `heif` extra is installed alongside
a pixel feature. The
default metadata path scans these containers without that plugin. A corrupt or
truncated file may still fail to decode.

### Some metadata removal requires ffmpeg

WebM, Matroska, MP3, WAV, FLAC, OGG, Opus, and AAC container metadata is stripped
through ffmpeg with stream copying. The operation fails if ffmpeg is absent or
cannot parse the input.

### Video pixel removal is provider-specific

The `video metadata` command and high level video API inspect and strip
supported AI provenance metadata without transcoding streams.

`video visible` and `remove_video_visible` additionally support the moving
Sora 2 mascot and wordmark, the current Veo four-point diamond, the legacy
`Veo` text, the Seedance boxed `AI` label, the fixed `Dola AI` text, the Hailuo
MINIMAX/Hailuo composite label, and the bottom-right Kling label with its
version suffix. Detection requires a recurring visual candidate across
adjacent frames. Fixed-mark candidates must remain anchored rather than
drifting with a scene object. Kling also requires a bright low-saturation
candidate near the expected frame edge. Provider provenance can recover
low-contrast runs only after visual evidence exists for the marks that define a
provenance prior, so metadata alone does not erase a clean API export.
The default auto-router evaluates all detectors in one decode pass but does not
rank their raw confidence values. Those scores are provider-specific and known
to cross-match in some layouts, so the router applies the independent temporal
policies and selects the first stable result in specificity order. Use an
explicit mark when the provider is already known.
Historical Sora Turbo exports use a small OpenAI swirl in the corner rather
than the moving mascot-and-wordmark design; that earlier variant is not
detected by the `sora` video mark. Hailuo and Kling coverage is specific to the
verified lower-edge layouts; a new provider layout needs a separate calibrated
silhouette. Other provider video labels are not supported yet. Google video
SynthID has an oracle-certified VAE removal path, while other proprietary
invisible video watermarks have no registered attack.

Visible removal transcodes the video stream and copies the complete audio
stream without shortening an audio tail. Completed visible and invisible
encodes are published atomically, so an encode failure preserves an existing
output. Visible removal now applies a guarded motion-compensated blend after
the per-frame fill. It uses adjacent optical flow only when the warped prior
mask covers the current mask and a source-context ring agrees; scene cuts and
disjoint marks keep the independent fill. This reduces measured paired
temporal error, but it is not a generative video-inpainting model and cannot
recover structure that no frame exposes. OpenCV can still leave a visible
smear where the mark overlaps a hard edge or structured texture. MI-GAN
improves difficult individual frames and is the practical learned CPU tier.
LaMa remains an offline quality option: a full real sequence confirmed its
multi-GB memory use and CPU throughput unsuitable for an online worker. The Veo diamond
uses a shape mask to limit damage outside the symbol. Seedance fills the full
localized box because a synthetic outline mask left part of the real
translucent border visible in an end-to-end check. OpenCV may therefore soften
texture inside that small box; use MI-GAN or LaMa when reconstruction quality
matters. Relative variable-frame timestamps are preserved through a timestamped
NUT bridge. A non-zero absolute video start PTS and the corresponding copied
audio offset are preserved through ffmpeg timestamp passthrough.
The encoder is source-aware for common 8-bit inputs: it probes and preserves
supported chroma sampling, recognized color metadata, encoder time base, and
MP4/MOV track timescale instead of accepting ffmpeg's implicit `yuv444p`
raw-BGR output. OpenCV still decodes through 8-bit BGR, so HDR/high-bit-depth
inputs are rejected before encoding rather than silently falling back to
`yuv420p`. The synthetic
Sora/OpenCV full-clip CI gate covers complete removal, untouched-region PSNR,
frame count, frame rate, duration, copied-audio identity, source stream
properties, paired temporal deltas against an independently encoded
frame-local baseline inside the filled region, and metadata
stripping through real ffmpeg. A second synthetic VFR clip verifies all display
timestamps to the source time-base tick, including a non-zero source start,
and checks both video and audio stream offsets. A separate constant-rate case
guards the non-zero-start routing independently of VFR detection. A local
full-sequence audit over all six providers passed both OpenCV and MI-GAN for
complete-frame removal, quiet second detection, stream starts, duration, and
copied audio. A full LaMa sequence passed the same checks but established that
the backend belongs in the offline tier on CPU. These bounded local checks are
still not universal evidence for every provider layout or source.

Native TC260 metadata in MP4/MOV is supported at its normative
`moov.udta.meta.keys/ilst` placement, including non-faststart files whose
`moov` follows a large media payload. MKV/WebM is supported at the normative
`Segment.Tags.Tag.SimpleTag` placement and uses ffmpeg for stream-copy removal.
AVI is supported at `LIST/INFO/AIGC`, and FLV at
`script.onMetaData.AIGC`; both use ffmpeg stream-copy removal. Stock ffmpeg can
write and strip the FLV form, but writing the nonstandard AVI child for fixture
generation requires dedicated muxer support, so the AVI reader is verified
against an exact synthetic RIFF structure.

MP4/MOV/M4V metadata removal now stream-copies the container in bounded chunks,
keeps every box size and media offset fixed, and publishes atomically. The
large-`mdat` regression rejects a full-source `read_bytes()` call and verifies
that the encoded payload is byte-identical. HEIF/AVIF/JPEG-XL image metadata
still uses the in-memory path because their XMP/EXIF items may live inside
`mdat`/`idat` and require bounded format-item parsing before that path can
stream safely.

### Metadata transformation is fail safe

`remove_ai_metadata` may copy an undecodable file through unchanged instead of
raising. User facing callers must use `strip_and_verify` and inspect its
surviving marker mapping before reporting success. `strip_and_verify` recovers
when `image_io` can still decode the raster by normalizing the container and
checking again. A truly undecodable file still reports the surviving markers.
The CLI uses this verified path.

### Sixteen bit PNG output is not preserved

The Pillow based PNG metadata rewrite uses the normal image save path and may
reduce a sixteen bit PNG to eight bits. A byte-level PNG metadata stripper
would be required to preserve that bit depth.

## Detection extras

The `detect` extra decodes an open DWT-DCT watermark used in some Stable
Diffusion, SDXL, and FLUX workflows. That decoder is sensitive to the carrier
and transformations. A negative result is not a universal negative.

The `trustmark` extra adds Adobe TrustMark decoding. The implementation retains
an additional JPEG re-encode gate because isolated decoder hits can otherwise
be content noise.

External AI versus real image classifiers are out of scope. The project
identifies concrete local provenance signals instead of shipping a generic
statistical classifier.

## Output and traceability

Removing file-local signals does not remove:

- provider account history;
- server side copies or provenance stores;
- perceptual fingerprints;
- evidence that an image passed through a removal pipeline;
- legal disclosure duties.

See [scope, safety, and legal notes](legal-and-safety.md).