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TrustMark C++

This C++17 runner uses OpenCV for image loading, crop/resize, and writing, and ONNX Runtime for the TrustMark B, C, P, and Q encoder and decoder models. Like the Rust and Python interfaces, it supports binary payload encoding and decoding with BCH error correction. Text encoding and watermark removal are not implemented.

Build​

Install an ONNX Runtime C/C++ distribution with headers and a shared library, and OpenCV with CMake packages. From the repository root:

cmake -S cpp -B cpp/build -DONNXRUNTIME_ROOT=/path/to/onnxruntime
cmake --build cpp/build

If CMake cannot find OpenCV, also pass -DOpenCV_DIR=/path/to/opencv/lib/cmake/opencv4. Fetch ONNX models from the rust/ directory with cargo xtask fetch-models (instructions).

Encode and decode images​

cpp/build/trustmark-runner encode-image rust/models Q images/ghost.png encoded.png \
  1011011110011000111111000000011111011111011100000110110110111 BCH_5
cpp/build/trustmark-runner decode-image rust/models Q encoded.png

The payload is a binary string of at most 40 (BCH_SUPER), 61 (BCH_5, the default), 68 (BCH_4), or 75 (BCH_3) bits. Short payloads are padded with zeros. The variant can be B, C, P, or Q. Optionally supply a strength in [0,1] after the BCH version; the default is 0.95. The output is an image file; use PNG for lossless roundtrips. The original alpha channel is preserved for RGBA inputs. Decode prints Found watermark (BCH_5): <payload bits> when BCH validation succeeds, or Corrupt or missing watermark otherwise.

OpenCV accepts 8-bit RGB/RGBA images here. The runner center-crops P images and images with an aspect ratio above 2:1, resizes to the ONNX model's input size (224 for P decoding, 256 otherwise), and normalizes RGB channels to [-1,1]. For encoding, it scales and clamps the model residual before compositing it onto the original image. OpenCV and the Rust/Python image resize routines can produce slightly different pixel values. The Python PyTorch decoder currently uses 245 for B/C/Q; the ONNX models used here and by Rust require 256.

Raw model inference​

The tensor interface remains available for callers with their own image pipeline:

cpp/build/trustmark-runner inspect rust/models/decoder_Q.onnx
cpp/build/trustmark-runner decode rust/models/decoder_Q.onnx input.f32
cpp/build/trustmark-runner encode rust/models/encoder_Q.onnx input.f32 "$BITS100" output.f32

input.f32 is a native-endian float32 RGB tensor in NCHW order, normalized to [-1,1]. Raw decode prints 100 unvalidated bits. Raw encode takes a full 100-bit packet including BCH parity and schema bits, and writes the model's float32 output tensor. Use encode-image/decode-image for BCH and image I/O.

To run the BCH checks and image inference smoke test:

ctest --test-dir cpp/build --output-on-failure
python3 cpp/tests/smoke.py cpp/build/trustmark-runner rust/models

To verify interoperability in both directions with the Rust CLI for B, C, P, and Q on square PNG and JPEG inputs, plus P and Q on an image wider than 2:1 (writing lossless PNG outputs):

cargo build --manifest-path rust/Cargo.toml -p trustmark-cli
python3 cpp/tests/interoperability.py cpp/build/trustmark-runner \
  rust/target/debug/trustmark rust/models

The Rust implementation does not reliably decode its own B/C watermarks on very wide images, so those cases are not used as cross-implementation fixtures.