feat(metadata): parse C2PA JUMBF manifest fields, add Images 2.0 sample, bump to 0.3.4

- metadata --check now shows claim_generator, c2pa_spec, digital_source_type,
  c2pa_actions, signer instead of empty table for C2PA-only files
- reuses existing extract_c2pa_chunk() from noai/c2pa.py — no more duplicate
  PNG chunk parsing or full-file reads
- adds data/samples/openai-images-2/amur-leopard.png: real gpt-image-2 output
  with C2PA manifest signed by OpenAI OpCo LLC / Trufo CA (spec 2.2.0)
- removes stale data/samples/nano-banana-1/2.png (no longer referenced)
- updates README: new Images 2.0 row in supported models table
- documents known text-degradation limitation in CLAUDE.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

CLAUDE.md

@@ -5,8 +5,21 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r
 ## How to run
 
 - `uv run remove-ai-watermarks all <image.png> -o <output.png>`
+- `uv run remove-ai-watermarks metadata <image.png> --check` — inspect AI metadata (C2PA, EXIF, PNG chunks)
+- `uv run remove-ai-watermarks metadata <image.png> --remove -o <out.png>` — strip all AI metadata
 
 ## Configuration
 
 - GPU/ML modules (invisible_engine, ctrlregen, watermark_remover) are optional — guard imports with `is_available()` checks
 - Tests for ML modules are limited to availability checks (require multi-GB downloads)
+
+## Key modules
+
+- `noai/c2pa.py` — PNG chunk parser; use `extract_c2pa_chunk(path)` to get raw caBX payload, `has_c2pa_metadata(path)` to detect. Do not reimplement chunk parsing.
+- `noai/constants.py` — PNG_SIGNATURE, C2PA_CHUNK_TYPE, C2PA_SIGNATURES constants
+- `face_protector.py` — YOLO detect + soft-blend pattern; mirror this for any "protect region during diffusion" features
+
+## Known limitations
+
+- `invisible` pipeline downscales to 768 px before diffusion → degrades fine text in infographics. Tracked; fix is tile-based or skip-downscale approach.
+- Pyright first run is slow (2-3 min) due to ML deps (torch/diffusers/transformers stubs)

README.md

@@ -29,6 +29,7 @@ Strips SynthID, C2PA Content Credentials, EXIF/XMP "Made with AI" labels, and vi
 | --- | --- | --- | --- | --- |
 | **Google Gemini / Nano Banana** | ✅ Sparkle logo | ✅ SynthID | ✅ C2PA + EXIF | Alpha reversal + diffusion + metadata strip |
 | **OpenAI DALL-E 3 / ChatGPT** | — | — | ✅ C2PA manifest | Metadata strip |
+| **OpenAI ChatGPT Images 2.0** (gpt-image-2) | — | ⚠️ imperceptible pixel watermark (no public detector yet) | ✅ C2PA manifest (verified) | Diffusion regeneration + metadata strip |
 | **Stable Diffusion (AUTOMATIC1111, ComfyUI)** | — | ✅ DWT / steganographic | ✅ PNG text chunks | Diffusion regeneration + metadata strip |
 | **Adobe Firefly** | — | — | ✅ Content Credentials (C2PA) | Metadata strip |
 | **Midjourney** | — | — | ✅ EXIF + XMP (prompt, model, seed) | Metadata strip |

data/samples/openai-images-2/README.md

@@ -0,0 +1,41 @@
+# OpenAI ChatGPT Images 2.0 sample
+
+Reference image generated by OpenAI's ChatGPT Images 2.0 (`gpt-image-2`, launched 2026-04-21). Kept in the repo so the C2PA manifest parser and invisible-watermark pipeline can be re-verified against a real production output.
+
+## `amur-leopard.png`
+
+- Resolution: 1055 x 1491, PNG, 3.0 MB
+- Downloaded: 2026-04-22
+- Content: AI-generated infographic about the Amur leopard (*Panthera pardus orientalis*), chosen to exercise the model's new accurate-text-rendering feature
+
+### Embedded C2PA manifest (caBX chunk, 23607 bytes)
+
+Parsed by `remove-ai-watermarks metadata --check`:
+
+| Field | Value |
+| --- | --- |
+| `claim_generator` | GPT-4o |
+| `c2pa_spec` | 2.2.0 |
+| `digital_source_type` | http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia |
+| `c2pa_actions` | created, converted |
+| Signer | OpenAI OpCo, LLC / OpenAI Media Service API |
+| Claim signing CA | Trufo C2PA Claim Signing CA (2025) -> Trufo C2PA Root CA (ECC P384) |
+| Timestamp authority | OpenAI TSA Issuing CA -> OpenAI TSA Root CA |
+
+The `trainedAlgorithmicMedia` tag is what triggers "Made with AI" labels on Instagram, Facebook, and X.
+
+### Invisible pixel-level watermark
+
+OpenAI's system card for Images 2.0 states the model embeds an "imperceptible, robust, and content-specific" pixel-level watermark alongside C2PA. No public detector exists, so bypass cannot be verified empirically for this sample.
+
+## Reproducing the removal
+
+```bash
+# C2PA strip only
+remove-ai-watermarks metadata amur-leopard.png --remove -o amur-leopard.clean.png
+
+# Full pipeline (visible + diffusion regeneration + metadata)
+remove-ai-watermarks all amur-leopard.png -o amur-leopard.all.png --device mps
+```
+
+Note: the diffusion step downscales to 768 px, which degrades fine text on text-heavy infographics like this one. Tracked as a known limitation; see project TODO.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "remove-ai-watermarks"
-version = "0.3.3"
+version = "0.3.4"
 description = "Remove visible and invisible AI watermarks from images (Gemini / Nano Banana, ChatGPT, Stable Diffusion)"
 readme = "README.md"
 requires-python = ">=3.10"

src/remove_ai_watermarks/__init__.py

@@ -1,3 +1,3 @@
 """Remove-AI-Watermarks: Unified tool for removing visible and invisible AI watermarks."""
 
-__version__ = "0.3.3"
+__version__ = "0.3.4"

src/remove_ai_watermarks/metadata.py

@@ -116,6 +116,82 @@ def has_ai_metadata(image_path: Path) -> bool:
     return False
 
 
+def _scan_png_c2pa_chunk(image_path: Path) -> dict[str, str]:
+    """Extract a human-readable summary of the C2PA manifest in a PNG file.
+
+    PIL does not expose the caBX JUMBF box via ``img.info``, so we delegate
+    chunk extraction to the existing ``extract_c2pa_chunk`` helper and pull
+    key fields from the JUMBF payload without a full CBOR parser.
+    """
+    import re
+
+    from remove_ai_watermarks.noai.c2pa import extract_c2pa_chunk
+
+    raw = extract_c2pa_chunk(image_path)
+    if raw is None:
+        return {}
+
+    # extract_c2pa_chunk returns chunk_header (8 bytes) + data + crc (4 bytes).
+    payload = raw[8:-4]
+    result: dict[str, str] = {"c2pa_manifest": f"C2PA manifest ({len(payload)} bytes)"}
+
+    def _cbor_text_after(key: bytes) -> str | None:
+        """Return the CBOR text-string immediately following ``key``.
+
+        Handles CBOR major-type 3 length prefixes: direct (0x60-0x77),
+        1-byte (0x78 NN), and 2-byte (0x79 NN NN).
+        """
+        idx = payload.find(key)
+        if idx < 0:
+            return None
+        p = idx + len(key)
+        if p >= len(payload):
+            return None
+        head = payload[p]
+        if 0x60 <= head <= 0x77:
+            length, start = head - 0x60, p + 1
+        elif head == 0x78 and p + 1 < len(payload):
+            length, start = payload[p + 1], p + 2
+        elif head == 0x79 and p + 2 < len(payload):
+            length, start = (payload[p + 1] << 8) | payload[p + 2], p + 3
+        else:
+            return None
+        raw_str = payload[start : start + length]
+        try:
+            return raw_str.decode("utf-8")
+        except UnicodeDecodeError:
+            return raw_str.decode("latin1", errors="replace")
+
+    if generator := _cbor_text_after(b"name"):
+        result["claim_generator"] = generator
+
+    if spec := _cbor_text_after(b"specVersion"):
+        result["c2pa_spec"] = spec
+
+    dst_match = re.search(
+        rb"(http://cv\.iptc\.org/newscodes/digitalsourcetype/[A-Za-z0-9_-]+)",
+        payload,
+    )
+    if dst_match:
+        result["digital_source_type"] = dst_match.group(1).decode("latin1")
+
+    actions = sorted(
+        {m.decode("latin1") for m in re.findall(rb"c2pa\.(created|converted|edited|opened|placed)", payload)}
+    )
+    if actions:
+        result["c2pa_actions"] = ", ".join(actions)
+
+    # Scan cert DN printable strings for the signer org name.
+    signer_match = re.search(
+        rb"([A-Za-z][A-Za-z0-9 .,&'()\-]{2,48}OpenAI[A-Za-z0-9 .,&'()\-]{0,48})",
+        payload,
+    )
+    if signer_match:
+        result["signer"] = signer_match.group(1).decode("latin1").strip()
+
+    return result
+
+
 def get_ai_metadata(image_path: Path) -> dict[str, str]:
     """Extract AI-related metadata from an image.
 
@@ -139,6 +215,7 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]:
                 else:
                     result[key] = str(value)
 
+    result.update(_scan_png_c2pa_chunk(image_path))
     return result
 
 

uv.lock

@@ -2015,7 +2015,7 @@ wheels = [
 
 [[package]]
 name = "remove-ai-watermarks"
-version = "0.3.3"
+version = "0.3.4"
 source = { editable = "." }
 dependencies = [
     { name = "click" },