Emit the Content Seal as its own signal on standalone-AI-tag files

The strength router already bets that a file whose only provenance is
the standalone AI digital-source tag is Meta Muse output (C2PA issuers
win first), and Muse stamps every output with the invisible Content
Seal. identify now surfaces that same bet as evidence: the additive
content_seal signal (medium confidence - an attribution, not a decode,
since no public decoder exists) plus the watermark string, emitted on
exactly the standalone_iptc condition that routes the cohort. Clients
select pixel removal from the signal list instead of parsing caveats,
the way InvisMark is additive over soft_binding. The API's invisible
gate already runs on ai_from_metadata, so all/invisible behavior is
unchanged; has_invisible_target needs no edit.

docs/supported-signals.md

@@ -152,7 +152,11 @@ inspection cannot independently verify the output. Microsoft's official
 [Content Provenance Detection API](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection)
 is the external oracle: it reports pixel `Watermark` and embedded `C2PA` results
 separately; a control-positive, output-negative pair is the available per-file
-verification path.
+verification path. The API needs Azure credentials; the page a human can check
+without an account is <https://ai.azure.com/nextgen/validate>. Its verdict is
+weaker than the API's: it collapses watermark and C2PA into one rendered result
+and tops out at `Inconclusive` rather than a watermark-negative, so treat
+`Inconclusive` on a processed file as "not confirmed", not as "detected still".
 
 Meta Muse Image stamps every output with Content Seal, a proprietary invisible
 pixel watermark, and ships no visible mark (the legacy `Imagined with AI`
@@ -161,7 +165,12 @@ This project has no local Content Seal decoder. Meta Model API outputs and
 Meta CDN copies carry an XMP `iptcExt:DigitalSourceType =
 trainedAlgorithmicMedia` companion tag, which `identify` reports through the
 existing Made-with-AI path; that IPTC code is a standard, not a Meta-exclusive
-signal, so it cannot key a strength cohort the way the C2PA issuer does. The
+signal, so it cannot key a strength cohort the way the C2PA issuer does. Since
+0.33.0 a standalone AI digital-source tag (no C2PA manifest) additionally emits
+the additive `content_seal` signal - the strength router's Meta bet as evidence,
+medium confidence, with the same caveat - so clients select pixel removal from
+the signal list exactly the way InvisMark is additive over `soft_binding`. It
+is an attribution, never a decode. The
 external oracle is `https://meta.ai/identification`: anonymous, no login,
 accepts image, video, and audio, enforces an unspecified daily identification
 limit, and answers with model attribution (`Muse Image 1 - Meta`) plus a

docs/watermarking-landscape.md

@@ -66,7 +66,7 @@ payloads. Removal remuxes either container through ffmpeg with stream copy.
   encoder and the carrier remains decodable.
 - **Invisible but NOT locally detectable (proprietary, API/oracle only — same wall as SynthID):** Amazon Titan Image Generator + Nova Canvas (Bedrock `DetectGeneratedContent` API), Kakao (new SynthID image adopter, May 2026), NVIDIA Cosmos (SynthID video). No local detector possible; treat like SynthID.
 - **C2PA 2.4 "Durable Content Credentials" (April 2026; verified against the spec) raise the bar for metadata stripping.** 2.4 defines soft bindings (an invisible watermark or a content fingerprint) plus a server-side manifest repository and a new `c2pa.repository-receipt` assertion. Per the spec: "if a C2PA manifest is removed from an asset, but a copy of that manifest remains in a provenance store elsewhere, the manifest and asset may be matched using available soft bindings." So our local `metadata --remove` deletes the *embedded* manifest, but a fingerprint/watermark soft binding can still re-link the image to its manifest in a repository server-side. Stripping the file is becoming necessary-but-not-sufficient against durable provenance. (Our parsers target the stable embedded-manifest format documented in C2PA 2.1 §11; that format is unchanged in 2.4 -- the new pieces are repository/soft-binding infra, not the on-file box layout, so no parser change is implied.) Spec: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html We now READ the soft-binding `alg` (`C2PA_SOFT_BINDINGS` / `soft_binding_vendors_in`) to name the forensic-watermark vendor, preserve the structured assertion's signed `value`, and locally DECODE the one open scheme, Adobe TrustMark (`trustmark_detector`); the rest stay name-only unless a compatible decoder is independently verified.
-- **Microsoft Paint and Photos InvisMark (reverse-engineered 2026-08-20):** Paint receives a per-generation GUID from remote prompt moderation, embeds it into locally generated pixels, and records the same value in `c2pa.soft-binding` under `com.microsoft.invismark.1`. The C2PA soft-binding registry independently identifies that algorithm as Microsoft Responsible AI InvisMark for image and video. Paint's 144-bit writer framing does not match the public repository's 100-bit pretrained checkpoint interface, so compatibility is not assumed. The parser reports the signed identifier; there is no validated local pixel decoder. Microsoft's external Content Provenance Detection API is the removal oracle because it reports `Watermark` separately from `C2PA`; a metadata-stripped, pixel-identical control must remain watermark-positive before an output-negative result is attributed to pixel removal. Sources: https://xusheng.dev/posts/reversing/mspaint_invisible_watermark/main/, https://github.com/c2pa-org/softbinding-algorithm-list/blob/main/softbinding-algorithm-list.json, and https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection
+- **Microsoft Paint and Photos InvisMark (reverse-engineered 2026-08-20):** Paint receives a per-generation GUID from remote prompt moderation, embeds it into locally generated pixels, and records the same value in `c2pa.soft-binding` under `com.microsoft.invismark.1`. The C2PA soft-binding registry independently identifies that algorithm as Microsoft Responsible AI InvisMark for image and video. Paint's 144-bit writer framing does not match the public repository's 100-bit pretrained checkpoint interface, so compatibility is not assumed. The parser reports the signed identifier; there is no validated local pixel decoder. Microsoft's external Content Provenance Detection API is the removal oracle because it reports `Watermark` separately from `C2PA`; a metadata-stripped, pixel-identical control must remain watermark-positive before an output-negative result is attributed to pixel removal. The no-account web page for the same check is <https://ai.azure.com/nextgen/validate> (its `Inconclusive` verdict is weaker than the API's separate watermark result). Sources: https://xusheng.dev/posts/reversing/mspaint_invisible_watermark/main/, https://github.com/c2pa-org/softbinding-algorithm-list/blob/main/softbinding-algorithm-list.json, and https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection
 - **Built in the dated batch:** soft-binding vendor detection, IPTC Photo
   Metadata AI-disclosure fields, C2PA detection and stripping for supported
   ISOBMFF video, the optional Adobe TrustMark decoder, and temporally stabilized

pyproject.toml

@@ -4,7 +4,7 @@
 # on it, including the ComfyUI node package. The console script below carries
 # the same weight, since users have it on PATH.
 name = "remove-ai-watermarks"
-version = "0.32.0"
+version = "0.33.0"
 description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
 readme = "README.md"
 requires-python = ">=3.11,<3.15"

src/remove_ai_watermarks/__init__.py

@@ -32,7 +32,7 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.32.0"
+__version__ = "0.33.0"
 
 __all__ = [
     "BatchSummary",

src/remove_ai_watermarks/_internal/c2pa.py

@@ -426,6 +426,11 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
     source_types: list[str] = []
     soft_binding_algorithms: list[str] = []
     soft_binding_values: list[str] = []
+    # Raw signer/generator identity strings, used to scope SynthID evidence to
+    # the vendor that actually asserted the manifest. A vendor token appearing
+    # anywhere else in the chain (e.g. Microsoft Designer's "Azure OpenAI
+    # ImageGen" softwareAgent) is a service name, not that vendor's provenance.
+    identity_strings: list[str] = []
     claim_generator_asserts_ai = False
 
     def add_tool_matches(value: str, *, asserts_ai: bool = False) -> None:
@@ -443,9 +448,11 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
                 value = signature.get(key)
                 if isinstance(value, str):
                     issuers.extend(_ordered_matches(value.encode(), C2PA_ISSUERS))
+                    identity_strings.append(value)
 
         direct_generator = manifest.get("claim_generator")
         if isinstance(direct_generator, str):
+            identity_strings.append(direct_generator)
             add_tool_matches(direct_generator, asserts_ai=True)
 
         candidates = manifest.get("claim_generator_info")
@@ -456,6 +463,7 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
                 name = cast("dict[object, object]", candidate_value).get("name")
                 if isinstance(name, str):
                     add_tool_matches(name, asserts_ai=True)
+                    identity_strings.append(name)
 
         assertions = manifest.get("assertions")
         if not isinstance(assertions, list):
@@ -528,9 +536,14 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
     has_watermark_action = any(action.startswith("watermarked") for action in actions)
     if has_watermark_action:
         info["watermarked"] = True
-    if info.get("ai_source_kind"):
-        selected_bytes = json.dumps(chain, ensure_ascii=False).encode()
-        synthid = synthid_evidence_vendors_in(selected_bytes, has_watermark_action=has_watermark_action)
+    if info.get("ai_source_kind") and not soft_binding_algorithms:
+        # Evidence scope: only the signer/generator identity strings above, never
+        # the whole chain - a vendor named inside another vendor's manifest (the
+        # Designer case) must not turn into that vendor's SynthID provenance. A
+        # manifest that names its own forensic soft-binding algorithm carries
+        # that vendor's mark and is excluded from the generic inference entirely.
+        identity_bytes = json.dumps(identity_strings, ensure_ascii=False).encode()
+        synthid = synthid_evidence_vendors_in(identity_bytes, has_watermark_action=has_watermark_action)
         if synthid:
             info["synthid_vendors"] = synthid
             info["synthid_watermark"] = synthid_verdict(", ".join(synthid))
@@ -611,7 +624,11 @@ def _populate_registry_fields(buffer: bytes, info: dict[str, Any]) -> bool:
 
     if b"c2pa.watermarked" in buffer:
         info["watermarked"] = True
-    synthid = synthid_evidence_vendors_in(buffer, has_watermark_action=info.get("watermarked", False))
+    synthid = (
+        []
+        if soft_binding_vendors_in(buffer)
+        else synthid_evidence_vendors_in(buffer, has_watermark_action=info.get("watermarked", False))
+    )
     if ai_source and synthid:
         info["synthid_vendors"] = synthid
         info["synthid_watermark"] = synthid_verdict(", ".join(synthid))

src/remove_ai_watermarks/_internal/watermark_profiles.py

@@ -79,8 +79,9 @@ QWEN_ZIMAGE_GOOGLE_STRENGTH = 0.27
 # cross-source spread (0.00725) to the worst clean boundary: 0.0695 + 0.00725.
 QWEN_ZIMAGE_OPENAI_STRENGTH = 0.07675
 
-# Microsoft's public detector returned Inconclusive rather than an API-level
-# watermark-negative verdict. Three valid Paint sources first cleared at 0.04125,
+# Microsoft's public detector (https://ai.azure.com/nextgen/validate) returned
+# Inconclusive rather than an API-level watermark-negative verdict. Three valid
+# Paint sources first cleared at 0.04125,
 # 0.055, and 0.095. Add one full observed cross-source spread to the worst clean
 # boundary: 0.095 + (0.095 - 0.04125) = 0.14875, rounded up to 0.15. This is a
 # measured corpus margin, not a universal InvisMark threshold.

src/remove_ai_watermarks/identify.py

@@ -1273,7 +1273,19 @@ def _identify_from_evidence(
     # reusing the derived `has_c2pa` / `source_kind` above, which are broader:
     # the file path's answer must not move.
     trained_source = b"trainedAlgorithmicMedia" in head or b"TrainedAlgorithmicMedia" in head
-    if not synthid and trained_source and c2pa_marker_in(head) and (vendors := synthid_evidence_vendors_in(region)):
+    # Same suppression as every other inference site: bytes that name their own
+    # forensic soft-binding algorithm carry that vendor's mark, and the generic
+    # vendor-token inference must not add a second, differently-attributed
+    # invisible watermark (Microsoft Designer: "Azure OpenAI ImageGen" agent +
+    # the InvisMark watermarked action read as "SynthID per OpenAI").
+    soft_binding_vendors = soft_binding_vendors_in(region)
+    if (
+        not synthid
+        and trained_source
+        and c2pa_marker_in(head)
+        and not soft_binding_vendors
+        and (vendors := synthid_evidence_vendors_in(region))
+    ):
         synthid = synthid_verdict(", ".join(vendors))
     if synthid:
         watermarks.append(
@@ -1288,7 +1300,7 @@ def _identify_from_evidence(
     # ── C2PA soft-binding: a named forensic/third-party watermark vendor ─
     # (Adobe TrustMark, Digimarc, Imatag, ...). Present in the manifest even when
     # the watermark itself can't be decoded; names whose watermark stamped the pixels.
-    soft_binding = meta.get("soft_binding") or (", ".join(v) if (v := soft_binding_vendors_in(region)) else None)
+    soft_binding = meta.get("soft_binding") or (", ".join(soft_binding_vendors) if soft_binding_vendors else None)
     if soft_binding:
         soft_binding_algorithm = meta.get("soft_binding_algorithm") or info.get("soft_binding_algorithm")
         soft_binding_value = meta.get("soft_binding_value") or info.get("soft_binding_value")
@@ -1322,6 +1334,21 @@ def _identify_from_evidence(
     if standalone_iptc:
         signals.append(Signal("iptc", "digitalSourceType (Made with AI)", "high"))
         watermarks.append("IPTC digitalSourceType (Made with AI)")
+        # Muse Image stamps every output with the invisible Content Seal, and this
+        # tag is the only provenance such a file carries - the same measured bet
+        # the strength router makes (vendor_for_strength -> "meta"). Emit the seal
+        # as its own stable signal, the way InvisMark is additive over
+        # soft_binding, so clients select pixel removal from the signal list
+        # instead of parsing caveats. It is an attribution, not a decode: no
+        # public Content Seal decoder exists, hence "medium".
+        signals.append(
+            Signal(
+                "content_seal",
+                "Meta Muse Content Seal pixel watermark (attributed by the standalone AI digital-source tag)",
+                "medium",
+            )
+        )
+        watermarks.append("Invisible Content Seal watermark (Meta Muse attribution)")
         caveats.append(_IPTC_ONLY_CAVEAT)
         caveats.append(_CONTENT_SEAL_CAVEAT)
         if platform is None:

src/remove_ai_watermarks/metadata.py

@@ -839,6 +839,13 @@ def synthid_source(image_path: Path, *, c2pa_info: dict[str, Any] | None = None)
     ai_source = b"trainedAlgorithmicMedia" in data or b"TrainedAlgorithmicMedia" in data
     if not (has_c2pa and ai_source):
         return None
+    from remove_ai_watermarks._internal.c2pa import soft_binding_vendors_in
+
+    # A scan that names its own forensic soft-binding algorithm carries that
+    # vendor's mark; the generic vendor-token inference must not add a second,
+    # differently-attributed invisible watermark from the same bytes.
+    if soft_binding_vendors_in(data):
+        return None
     matched = synthid_evidence_vendors_in(data)
     return ", ".join(matched) if matched else None
 

tests/test_designer_synthid_suppression.py

@@ -0,0 +1,85 @@
+"""Regression: a manifest that names its own forensic soft binding must not
+also report a SynthID watermark from the generic vendor-token inference.
+
+Microsoft Designer manifests sign as Microsoft, carry the InvisMark
+``c2pa.watermarked`` action, and name their generation agent
+"Azure OpenAI ImageGen". The OpenAI issuer token inside that agent name plus
+the watermarked action used to satisfy the OpenAI SynthID-evidence rule,
+double-counting one forensic mark as two pixel watermarks.
+"""
+
+from __future__ import annotations
+
+from remove_ai_watermarks._internal.c2pa import c2pa_info_from_manifest_store
+
+DESIGNER_STORE = {
+    "active_manifest": "designer",
+    "manifests": {
+        "designer": {
+            "signature_info": {"issuer": "Microsoft Corporation", "common_name": "Microsoft Corporation"},
+            "claim_generator_info": [{"name": "Microsoft Responsible AI Provenance", "version": "1.0"}],
+            "assertions": [
+                {
+                    "label": "c2pa.actions",
+                    "data": {
+                        "actions": [
+                            {
+                                "action": "c2pa.created",
+                                "softwareAgent": {"name": "Azure OpenAI ImageGen"},
+                                "digitalSourceType": "http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia",
+                            },
+                            {"action": "c2pa.watermarked"},
+                        ]
+                    },
+                },
+                {
+                    "label": "c2pa.soft-binding",
+                    "data": {"alg": "com.microsoft.invismark.1", "blocks": [{"value": "bf7a2993-cc1f-47e1-b1f0-cd8839aabb22"}]},
+                },
+            ],
+        }
+    },
+}
+
+
+def test_named_soft_binding_suppresses_generic_synthid_evidence() -> None:
+    info = c2pa_info_from_manifest_store(DESIGNER_STORE)
+    assert info["ai_source_kind"] == "generated"
+    assert info["soft_binding_algorithm"] == "com.microsoft.invismark.1"
+    assert info.get("synthid_watermark") is None
+    assert info.get("synthid_vendors") is None
+
+
+def test_vendor_agent_name_alone_is_not_the_vendors_provenance() -> None:
+    """The identity-scoped inference must not fire on a service name either.
+
+    Same manifest without the soft binding: the "Azure OpenAI ImageGen" agent
+    is not an OpenAI signature or claim generator, so no OpenAI SynthID
+    evidence may be derived from it.
+    """
+    store = {
+        "active_manifest": "designer",
+        "manifests": {
+            "designer": {
+                "signature_info": {"issuer": "Microsoft Corporation", "common_name": "Microsoft Corporation"},
+                "claim_generator_info": [{"name": "Microsoft Responsible AI Provenance", "version": "1.0"}],
+                "assertions": [
+                    {
+                        "label": "c2pa.actions",
+                        "data": {
+                            "actions": [
+                                {
+                                    "action": "c2pa.created",
+                                    "softwareAgent": {"name": "Azure OpenAI ImageGen"},
+                                    "digitalSourceType": "http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia",
+                                },
+                                {"action": "c2pa.watermarked"},
+                            ]
+                        },
+                    }
+                ],
+            }
+        },
+    }
+    info = c2pa_info_from_manifest_store(store)
+    assert info.get("synthid_watermark") is None

tests/test_identify.py

@@ -666,6 +666,35 @@ class TestIdentifyRealSamples:
         assert r.is_ai_generated is True
         assert r.ai_source_kind == "enhanced"
 
+    def test_standalone_ai_tag_attributes_the_content_seal(self, tmp_path: Path):
+        """A standalone AI digital-source tag emits the seal as its own signal.
+
+        Muse Image outputs carry no C2PA; this tag is their only provenance, and
+        Muse stamps every output with the invisible Content Seal. The signal is
+        the strength router's Meta bet as evidence - an attribution, not a decode
+        (no public decoder exists), so its confidence is medium and the caveat
+        still points at the oracle.
+        """
+        p = tmp_path / "muse-tag.jpg"
+        p.write_bytes(
+            b'\xff\xd8\xff\xe1<x:xmpmeta Iptc4xmpExt:DigitalSourceType="trainedAlgorithmicMedia"></x:xmpmeta>\xff\xd9'
+        )
+
+        r = identify(p, check_visible=False, check_invisible=False)
+
+        names = [s.name for s in r.signals]
+        assert "iptc" in names
+        assert "content_seal" in names
+        seal = next(s for s in r.signals if s.name == "content_seal")
+        assert seal.confidence == "medium"
+        assert "Invisible Content Seal watermark (Meta Muse attribution)" in r.watermarks
+        assert any("meta.ai/identification" in c for c in r.caveats)
+
+    def test_c2pa_backed_file_gets_no_content_seal_attribution(self):
+        """C2PA issuers win first: a manifest-backed file is not Meta-routed."""
+        r = identify(SAMPLES_DIR / "flux-1.png", check_visible=False, check_invisible=False)
+        assert "content_seal" not in [s.name for s in r.signals]
+
     def test_flux_bfl_c2pa_png(self):
         # flux-1.png: real Black Forest Labs FLUX.2 Playground output (signed C2PA).
         r = identify(SAMPLES_DIR / "flux-1.png", check_visible=False)

uv.lock

@@ -288,7 +288,7 @@ wheels = [
 
 [[package]]
 name = "c2pa-python"
-version = "0.37.7"
+version = "0.37.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cryptography" },
@@ -298,15 +298,15 @@ dependencies = [
     { name = "toml" },
     { name = "wheel" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/58/74/291b19f26fe628ca6bf001be741dfe634f8689756cf8495499e02ab2f631/c2pa_python-0.37.7.tar.gz", hash = "sha256:66114c3bc6b073249cb33043e491cf292e052ff770c1aefaed66a5ca2d8aaa0c", size = 112987, upload-time = "2026-08-13T21:25:27.464Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1f/32/e93959d17db5af4078532f587b9638e87b6429ca0b51348a8ac6ad99e471/c2pa_python-0.37.8.tar.gz", hash = "sha256:c52eb07244558a5997fe61b6ed8d879b4f46f38642dd11956bcedf888c58649c", size = 113008, upload-time = "2026-08-27T20:40:06.081Z" }
 wheels = [
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