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 Historical measurements and superseded experiments live in the research archive listed in the documentation index.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

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 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.