remove-ai-watermarks/docs/python-api.md

# Python API

Use the high level API for normal application integration. Low level detector
and pipeline modules are intended for maintainers and specialized workflows.

Dependency groups are identical for the CLI and Python API. The default install
covers metadata extraction, normalization, verdict logic, and stripping.
Array/pixel APIs use `pixels`; visible removal uses `visible`; DWT-DCT detection
uses `detect`; invisible image removal uses `qwen-zimage` and an NVIDIA GPU; and
visible video processing uses `video`. Video SynthID removal is a separate VAE
path that still runs on CPU and combines `video` and `diffusion`. Add `heif`
independently when path-based pixel APIs must decode HEIC, HEIF, or AVIF. See
the complete [feature-extra matrix](installation.md#feature-extras).

## Remove visible marks

Install `remove-ai-watermarks[visible]` before using the visible-removal API.

```python
import remove_ai_watermarks as raiw

result, removed = raiw.remove_visible(
    "watermarked.png",
    "clean.png",
)
```

The function returns:

- the result as a BGR NumPy array;
- a list of labels that were removed.

An empty `removed` list means that no registered visible mark was selected. It
does not prove the image has no metadata or invisible watermark.

### Path input

For a path input, `remove_visible`:

- reads metadata provenance for the default `auto` sensitivity;
- preserves a separate alpha channel;
- writes the output when an output path is supplied;
- strips AI metadata from the written output by default;
- preserves the original bytes for a same-format no-op copy.

```python
result, removed = raiw.remove_visible(
    "watermarked.png",
    "clean.png",
    sensitivity="auto",
    backend="auto",
    strip_metadata=True,
)
```

Set `write_noop=False` if the output path must remain untouched when nothing is
removed:

```python
result, removed = raiw.remove_visible(
    "input.png",
    "clean.png",
    write_noop=False,
)
```

### Array input

Array inputs are BGR NumPy arrays. They do not carry file metadata or a separate
alpha plane:

```python
import cv2
import remove_ai_watermarks as raiw

image = cv2.imread("input.png")
result, removed = raiw.remove_visible(image, backend="cv2")
```

## Run the full pipeline

`remove_all` is the library form of the `all` command: visible marks, then the
invisible watermark, then AI metadata. Stages are chained through a file in the
system temp directory, so a partial result never appears at the output path.

```python
import remove_ai_watermarks as raiw

result = raiw.remove_all("input.png", "clean.png")   # -> RemoveAllResult
print(result.output)          # the path written
print(result.visible_label)   # the marks removed, or None
print(result.invisible)       # "removed" | "no-signal" | "unavailable"
```

`invisible` is the field to check. `"unavailable"` means the GPU extra is not
installed, so the output *looks* processed but still carries the watermark;
`"no-signal"` means the scrub was deliberately skipped because nothing was
locally detectable, which is a successful run.

Pass `InvisibleOptions` to tune the diffusion stage, and `engine` to reuse one
loaded model across many calls:

```python
from remove_ai_watermarks import InvisibleOptions

raiw.remove_all(
    "input.png",
    "clean.png",
    invisible=InvisibleOptions(strength=0.35),
    force=True,
    progress=print,
)
```

`InvisibleOptions` carries only what `InvisibleEngine` itself takes, and uses the
engine's own parameter names and defaults. `force`, which decides whether the
engine runs at all, is a parameter of `remove_all` and `remove_batch` alongside
`backend` and `sensitivity`.

If AI metadata survives the strip, `remove_all` raises `MetadataStripIncomplete`
**before** writing anything: an AI-readable output is worse than no output.

`remove_batch` runs one mode over a directory and never lets a single bad file
end the run:

```python
summary = raiw.remove_batch("in_dir", "out_dir", mode="visible")   # -> BatchSummary
print(summary.processed, summary.failed, summary.errors)
print(summary.invisible_unavailable)   # outputs that still carry the watermark
```

`mode` is `all`, `visible`, `invisible`, or `metadata`. Pass a constructed
`InvisibleEngine` as `engine` to load the model once for the whole directory.

## Inspect provenance

The default installation evaluates file metadata. Add `visible`, `detect`, or
`trustmark` to enable the corresponding optional pixel signals.

Get the vendor keys used by visible removal:

```python
import remove_ai_watermarks as raiw

vendors = raiw.visible_provenance("input.png")
```

Get the full provenance report:

```python
from pathlib import Path

from remove_ai_watermarks.identify import identify

report = identify(Path("input.png"))
print(report.platform)
print(report.signals)
```

Use `check_visible=False` and `check_invisible=False` for metadata-only
inspection through the compatible path-based API:

```python
report = identify(
    Path("input.png"),
    check_visible=False,
    check_invisible=False,
)
```

Extraction and detection are also available as separate steps. This is useful
when a file-reading worker collects the metadata once and another component
evaluates the resulting evidence:

```python
from remove_ai_watermarks.identify import (
    extract_provenance_evidence,
    identify_from_evidence,
)

evidence = extract_provenance_evidence(Path("input.png"))
report = identify_from_evidence(evidence)
```

If metadata was collected by another component, normalize its nested record
without reopening the original file:

```python
from remove_ai_watermarks.identify import (
    evidence_from_metadata_record,
    identify_from_evidence,
)

record = {
    "pil": {"info:parameters": "Steps: 20, Sampler: Euler"},
    "exif": {"0th": {"Software": "Stable Diffusion"}},
}
evidence = evidence_from_metadata_record(record, path=Path("input.png"))
report = identify_from_evidence(evidence)
```

The normalizer recursively preserves text and byte values. It also decodes
strings prefixed with `hex:` and fields named `base64` or ending in
`_base64`. Diagnostic values under `error` and `kind` are ignored because they
describe the collector rather than the source file. Pass a C2PA manifest-store
dictionary in `record["c2pa_store"]`, or through the explicit
`c2pa_manifest_store` argument.

`identify_from_evidence` does not reopen the source file by default: it evaluates
metadata only, and registered visible marks and pixel-backed invisible watermarks
remain in the path-based `identify` call.

Pass `image_path` together with `check_visible` or `check_invisible` to add those
pixel detectors on top of the SAME evidence. That is how a caller asking one file
two provenance questions — which vendor is confirmed, and is there an invisible
target — pays for the metadata extraction once:

```python
from remove_ai_watermarks.identify import extract_provenance_evidence, identify_from_evidence

evidence = extract_provenance_evidence(source)
metadata_only = identify_from_evidence(evidence)
with_pixels = identify_from_evidence(evidence, image_path=source, check_invisible=True)
```

## Strip metadata

```python
from pathlib import Path

from remove_ai_watermarks.metadata import has_ai_metadata, strip_and_verify

source = Path("input.png")
output = Path("clean.png")

if has_ai_metadata(source):
    output_path, surviving_markers = strip_and_verify(source, output)
    if surviving_markers:
        raise RuntimeError(
            f"AI metadata remains in {output_path}: {surviving_markers}"
        )
```

Use `strip_and_verify` when your application reports that stripping succeeded.
It checks the written output and returns `(output_path, surviving_markers)`.
When the first strip leaves markers in a malformed but raster-decodable image,
it normalizes the container through `image_io` and checks again. That recovery
path preserves the pixels but drops standard metadata. Treat a nonempty
`surviving_markers` mapping as a failure.

`remove_ai_metadata` is the lower level fail-safe transformer. It may copy an
undecodable input through unchanged, so its return alone must not be presented
as proof that metadata was removed.

## Identify and clean video

The high level video API supports MP4, MOV, M4V, WebM, MKV, AVI, and FLV:
metadata-only calls work with the default install, while visible identification,
removal, and the complete pipeline require `remove-ai-watermarks[video]`.

```python
import remove_ai_watermarks as raiw

report = raiw.identify_video("input.mp4")
print(report.is_ai_generated)
print(report.platform)
print(report.visible_mark)
print(report.metadata_markers)
```

`identify_video` uses the same full-clip temporal arbiter as visible removal.
It reports a recurring registered mark and supported AI metadata as positive
signals. When neither is present, `is_ai_generated` is `None`, never `False`.
The absence of a public local video SynthID decoder is included in `caveats`.
Pass `check_visible=False` for a bounded metadata-only inspection.

For normal product integration, use the complete locally verifiable pipeline:

```python
result = raiw.remove_video_all("input.mp4", "clean.mp4")
if result.remaining_metadata:
    raise RuntimeError(f"AI metadata remains: {result.remaining_metadata}")
```

The default removes one stable supported visible provider mark when present,
always strips verified AI metadata, and writes a same-container output even
when neither signal is found. This gives callers one predictable output path.
It does not run lossy invisible regeneration by default.

`include_invisible=True` explicitly adds VAE regeneration for MP4, MOV, or M4V.
`VideoAllResult.invisible_removed` reports whether the oracle-certified SynthID
stage ran.

Process a top-level directory sequentially:

```python
batch = raiw.remove_video_batch("videos", "videos_clean", mode="all")
if batch.failed:
    for item in batch.items:
        if item.error:
            print(item.source, item.error)
```

Batch modes are `all`, `visible`, and `metadata`. Successful visible no-ops are
copied byte-for-byte, keeping the output directory complete. Per-file failures
are returned in `VideoBatchItem.error`; they do not discard successful outputs.
The invisible stage is available only as an explicit opt-in in `all` mode and
reuses one loaded VAE runtime across the batch.

## Inspect and strip video metadata

Metadata inspection and removal use the same supported video containers:

```python
import remove_ai_watermarks as raiw

report = raiw.inspect_video_metadata("input.mp4")
if report.has_ai_metadata:
    result = raiw.remove_video_metadata("input.mp4")
    if result.remaining:
        raise RuntimeError(f"AI metadata remains: {result.remaining}")
```

`remove_video_metadata` does not transcode video or audio streams. Its default
output is `input_clean.mp4`, leaving the source untouched. An explicit output
must use the same container extension as the source.

The returned `VideoMetadataResult` records the source, output, metadata detected
before removal, and any markers remaining after the verified strip. MP4/MOV
inspection recognizes the native TC260 `AIGC` entry in
`moov.udta.meta.keys/ilst`; its removal preserves container size and encoded
stream bytes. MP4/MOV/M4V are copied in bounded chunks, so a large `mdat` is not
loaded into memory; publication is atomic. MKV/WebM inspection recognizes the corresponding
`Segment.Tags.Tag.SimpleTag` representation; its removal requires ffmpeg for a
stream-copy remux. AVI inspection reads `LIST/INFO/AIGC`, and FLV inspection
reads `script.onMetaData.AIGC`; both use the same verified ffmpeg stream-copy
removal path.

## Remove video SynthID

Install `remove-ai-watermarks[video,diffusion]` before using the video SynthID
API.

```python
import remove_ai_watermarks as raiw

result = raiw.remove_video_invisible(
    "input.mp4",
    "clean.mp4",
    device="auto",
)
if result.remaining_metadata:
    raise RuntimeError(f"AI metadata remains: {result.remaining_metadata}")
```

`remove_video_invisible` supports MP4, MOV, and M4V. It regenerates the complete
video through a VAE in bounded batches, shares one seeded latent-noise field
across all frames, streams pixels to ffmpeg, copies complete audio, strips
source metadata, and publishes atomically. The default output is
`input_clean.mp4`; a distinct same-container output is required.

The returned `VideoInvisibleResult` includes output geometry, frame rate, frame
count, paired PSNR, and the motion-compensated temporal-residual ratio. Those
fields measure fidelity and flicker only. They are not a SynthID detector.
The default `noise_std=0.15` is the current full-clip oracle floor; `0.10`
remained detected on the public eight-second Veo calibration carrier.
The default profile is oracle-certified. Google does not publish a local
decoder for this video payload, so a fresh source-positive, output-negative
pair from Gemini's built-in SynthID verifier remains an optional per-file audit.
A response inferred from a visible logo or metadata is not such a verdict, and
an adversarial follow-up asking ordinary Gemini to reinterpret the verifier is
not a second oracle run.

## Remove a supported visible video mark

```python
import remove_ai_watermarks as raiw

result = raiw.remove_video_visible(
    "input.mp4",
    "clean.mp4",
    backend="cv2",
    strip_metadata=True,
    temporal_consistency=True,
)
if result.output is None:
    print("No temporally stable supported mark was found")
else:
    print(result.mark)

veo_result = raiw.remove_video_visible(
    "veo.mp4",
    "veo_clean.mp4",
    mark="veo",
)
seedance_result = raiw.remove_video_visible(
    "seedance.mp4",
    "seedance_clean.mp4",
    mark="seedance",
)
dola_result = raiw.remove_video_visible(
    "dola.mp4",
    "dola_clean.mp4",
    mark="dola",
)
hailuo_result = raiw.remove_video_visible(
    "hailuo.mp4",
    "hailuo_clean.mp4",
    mark="hailuo",
)
kling_result = raiw.remove_video_visible(
    "kling.mp4",
    "kling_clean.mp4",
    mark="kling",
)
```

`remove_video_visible` scans the complete video before writing output. It
combines synthetic multi-scale visual matching with temporal consistency, so an
isolated lookalike in one frame is not enough to authorize inpainting.
`mark="auto"` is the default: it evaluates all providers in one decode pass and
selects the first stable match in specificity order (`sora`, `veo`, `seedance`,
`dola`, `hailuo`, `kling`). Provider confidence values are calibrated
independently and are not compared across detectors. Pass one of those explicit
values to restrict the scan to a single provider. The Veo detector recognizes
the current four-point diamond and the
legacy `Veo` text. Seedance recognizes the boxed `AI` label, Dola recognizes
its compact text label, Hailuo recognizes the composite MINIMAX/Hailuo label,
and Kling recognizes its bottom-right logo, wordmark, and version suffix. Each
variant has an independent synthetic silhouette and calibrated temporal policy.
After each accepted frame is filled, `temporal_consistency=True` motion-aligns
the preceding accepted fill and blends it only when the warped prior mask
covers the current mask and a surrounding source-context ring agrees. Scene
cuts and disjoint masks keep the independent current fill. Pass
`temporal_consistency=False` for the frame-local baseline.

The returned `VideoVisibleResult` records the selected `mark`, the total,
detected, and removed frame counts, plus any AI metadata that survived the
output encode. The function returns `output=None` and writes no file when no
stable mark is selected. Video pixels are transcoded through ffmpeg while the
complete source audio stream is copied. The encoder preserves supported 8-bit
source chroma sampling, color tags, MP4/MOV track timescale, and relative
variable-frame timestamps. It also retains a non-zero source start PTS and the
copied audio offset. A failed encode preserves any existing output; only a
completed result is published atomically.
SDR 8-bit video is the supported pixel contract. High-bit-depth, PQ, and HLG
sources raise `RuntimeError` before encoding instead of being silently reduced
to 8-bit SDR.

## Remove invisible watermarks

Install `remove-ai-watermarks[qwen-zimage]`. Both profiles need it, and both
need an NVIDIA GPU.

```python
from pathlib import Path

from remove_ai_watermarks.invisible_engine import InvisibleEngine

engine = InvisibleEngine(
    pipeline="qwen-zimage",  # the default; the only other value is "sdxl-zimage"
    device=None,
    cpu_offload=False,
)

engine.remove_watermark(
    Path("watermarked.png"),
    Path("clean.png"),
)
```

`device=None` and `device="auto"` both run detection. `"cuda"` pins it without
detecting. Every other value raises at construction rather than deferring a
guaranteed failure to model-load time.

For limited CUDA memory:

```python
engine = InvisibleEngine(
    pipeline="qwen-zimage",
    cpu_offload=True,
)
```

Both profiles are CUDA-only, so on a machine without an NVIDIA GPU `device=None`
resolves to `cpu` and construction raises. For the SDXL global stage instead of
Qwen:

```python
engine = InvisibleEngine(pipeline="sdxl-zimage")
```

The `qwen-zimage` extra is required for both profiles: each runs the same
DiffSynth Z-Image face stage.

`remove_watermark` takes strength, seed, tiling, resolution, and postprocessing
controls. It takes no model id, step count or guidance scale, and neither does the
constructor: each profile pins its model stack, its per-stage schedule and CFG
1.0, so passing one raises `TypeError` at the call rather than being accepted and
refused several layers down. Read the method signature in
[`invisible_engine.py`](../src/remove_ai_watermarks/invisible_engine.py) or use
the CLI guide for the concepts.
Defaults can differ between the Python method and CLI profile resolution, so
pass values explicitly when reproducibility matters.