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Harden TrustMark detection with an official fixture
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@@ -7,3 +7,10 @@ This page records notices required by source dependencies and licensed derivativ
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- The DWT-DCT implementation derives from ShieldMnt's
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[`invisible-watermark`](https://github.com/ShieldMnt/invisible-watermark), licensed
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under MIT. Its notice ships in `src/remove_ai_watermarks/licenses/invisible-watermark-MIT.txt`.
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## Licensed test fixtures
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- `data/fixtures/provenance/adobe-trustmark-p.png` is Adobe's official
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TrustMark Variant P example, licensed under MIT. Its source commit, digest,
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and reproduced license are recorded beside the fixture in
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`data/fixtures/README.md` and `data/licenses/adobe-trustmark-MIT.txt`.
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@@ -344,8 +344,11 @@ Diffusion, SDXL, and FLUX workflows. That decoder is sensitive to the carrier
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and transformations. A negative result is not a universal negative.
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The `trustmark` extra adds Adobe TrustMark decoding. The implementation retains
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an additional JPEG re-encode gate because isolated decoder hits can otherwise
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be content noise.
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an additional JPEG re-encode gate and requires the binary payload and schema to
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remain identical because isolated decoder hits can otherwise be content noise.
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It accepts Variant P schemas 0-2. Variant Q requires a different model, and
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schema 3 is rejected at the measured precision threshold. The calibration
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history is in [module internals](module-internals.md#metadata-and-provenance).
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External AI versus real image classifiers are out of scope. The project
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identifies concrete local provenance signals instead of shipping a generic
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@@ -564,7 +564,13 @@ human-made verdict. `ai_source_kind` distinguishes fully generated content from
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AI-enhanced composites when the source metadata provides that distinction.
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TrustMark is reported as a watermark signal but does not by itself assert AI
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origin because it can also protect human-authored content.
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origin because it can also protect human-authored content. The decoder requests
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binary mode, matching Adobe's Durable Content Credentials example, then requires
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the same payload and schema after a quality-95 JPEG round-trip. Only Variant P
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schemas 0-2 count as positives. Schema 3 is below the precision threshold: all
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38 measured historical false-positive candidates used it, and six retained the
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same false payload after re-encoding. The official Adobe Variant P schema-1
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fixture in `data/fixtures/provenance/` is the positive regression control.
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Regression coverage:
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@@ -80,7 +80,9 @@ The inspection and stripping code handles signals in these groups:
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- Samsung AI editing markers;
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- Hugging Face job metadata;
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- open Stable Diffusion style DWT-DCT watermarks with the `detect` extra;
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- Adobe TrustMark with the `trustmark` extra.
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- Adobe TrustMark Variant P schemas 0-2 with the `trustmark` extra. Variant Q
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needs a different model, while schema 3 is deliberately rejected because it
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produced persistent false positives on unrelated generators.
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`identify` combines detected signals into a `ProvenanceReport`. It reports
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unknown when evidence is absent. It never treats missing metadata as proof that
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@@ -154,7 +156,7 @@ not a universal clean verdict.
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| OpenAI image generators | None registered | Diffusion regeneration for supported invisible signals | C2PA and generator provenance |
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| Stable Diffusion and SDXL | None registered | Diffusion regeneration; optional open decoder | Embedded parameters and text metadata |
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| FLUX | None registered | Diffusion regeneration; optional open decoder | C2PA for supported sources |
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| Adobe Firefly | None registered | No proprietary local decoder | C2PA; optional TrustMark decoder |
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| Adobe Firefly | None registered | Optional TrustMark Variant P decoder | C2PA |
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| Midjourney | None registered | No registered pixel decoder | EXIF, XMP, and IPTC signals |
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| ByteDance generators | Doubao and Jimeng marks | No registered pixel decoder | TC260 AIGC, supported C2PA, and exact app-export AIGC disclosures |
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| Qwen | Qwen mark | No registered pixel decoder | TC260 AIGC |
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