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 Historical measurements and superseded experiments live in the research archive listed in the documentation index.

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 dependency free. 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.

Strength is content and seed dependent

For SDXL and ControlNet, the CLI resolves an unset strength from the detected vendor:

• OpenAI: 0.10;
• Google: 0.15;
• unknown: 0.15.

An explicit --strength overrides these defaults. The defaults are operating points, not universal guarantees. Near a removal threshold, different content or a different random seed may change the verifier result.

The base Qwen and qwen-zimage profiles have profile specific strength behavior. Consult remove-ai-watermarks invisible --help and the source of watermark_profiles.py for the current resolver.

Pipelines have different quality tradeoffs

Pipeline	Main limit
controlnet	Edge conditioning can preserve a watermark carrying region too closely, and faces may drift.
sdxl	Flat graphics and precise structure may receive too little or unhelpful change.
qwen	Large CUDA oriented model; face smoothing can still be significant.
qwen-zimage	CUDA only, large model stack, and limited broad certification across seeds and content.

The legacy default profile name maps to sdxl. The --auto flag is deprecated, emits a warning, and changes nothing.

Resolution and memory

Small images are enlarged before SDXL based diffusion

The SDXL, ControlNet, and base Qwen paths use a default minimum long side of 1024. Smaller inputs are enlarged before diffusion and restored to their original dimensions afterward. Set --min-resolution 0 to disable the floor.

qwen-zimage does not apply this SDXL minimum resolution floor.

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 use a Pillow fallback

OpenCV does not decode these formats in the project. image_io.imread falls back to Pillow with pillow-heif. 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.

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. The CLI does this.

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.