Add model card

README.md

@@ -0,0 +1,74 @@
+---
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+  - watermarking
+  - diffusion
+  - provenance
+  - attribution
+  - stable-diffusion
+---
+
+# DiffSign public watermark decoder (256-bit)
+
+Public verification artifact for the anonymous **DiffSign** submission,
+*Public Authorization and the Adaptive Attack Surface of Diffusion-Watermark
+Attribution*.
+
+This repository contains **only the public decoder** `D_psi`. The secret
+encoder `E_phi` is server-private by design and is deliberately not published:
+DiffSign's threat model assumes an adversary holds the full public
+verification interface but cannot invoke the encoder.
+
+## Artifact
+
+| | |
+|---|---|
+| File | `diffsign_public_decoder_256bit.pth` |
+| Size | 21,702,122 bytes |
+| SHA-256 | `1cf105cd89de5aba6beefe5e48af2082de78de296e912557ab2d7905c2ab975c` |
+| Tensors | 38 (5,422,300 parameters) |
+| Output | logits `[batch, 256, 2]` |
+| Raw LLRs | `logit(bit=1) - logit(bit=0)`, shape `[batch, 256]` |
+
+Top-level fields: `format_version`, `artifact`, `decoder_state_dict`,
+`decoder_config`, `preprocessing`, `llr_convention`. It contains no
+`encoder_state_dict` and no optimizer, scheduler, or training state.
+
+## Preprocessing
+
+Pair the decoder with the public VAE from
+[`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5):
+
+1. Convert to RGB and resize to 512x512.
+2. Normalize pixels to `[-1, 1]`.
+3. Take the VAE posterior mode and multiply by the latent scaling factor.
+4. Evaluate `D_psi` once.
+5. Return `logit(bit=1) - logit(bit=0)` as `float32[256]` raw LLRs.
+
+Public verification is normative in `float32`. Half precision is permitted for
+throughput but is not the reference configuration: it shifts raw LLRs by up to
+`2e-2` and can flip a hard decision on a bit whose LLR is already near zero,
+so two verifiers on different hardware may disagree.
+
+## Usage
+
+```python
+from diffsign.physical import PublicWatermarkDecoder
+
+decoder = PublicWatermarkDecoder.from_pretrained(
+    "diffsign_public_decoder_256bit.pth",
+    device="cuda",
+)
+raw_llrs = decoder.decode_raw_llrs(image)   # float32[256]
+```
+
+The raw LLRs are divided by the fixed calibration temperature `0.7144` and
+decoded by a CRC-aided successive-cancellation list decoder (length-256
+non-systematic polar code, list size 8) into a 112-bit payload, which resolves
+through an audited transparency log to a signed receipt.
+
+## Scope
+
+Inference only. No training loop, dataset loader, attack implementation,
+third-party baseline, benchmark harness, or bulk generation script.