onnxruntime VS AppleNeuralHash2ONNX

ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.

tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.

- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.

The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.

[1]: https://www.circle-lang.org/

I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.

For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.

What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.

If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).

Perhaps you're referring to the hash collisions that were generated for the client-side NeuralHash which was reverse engineered about a year ago: https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX/issues/1

Except Apple’s hashes have collisions where images that are similar in content but not identical will have the same hash. Meaning their process will have the same result: benign images being flagged as CSAM. See an example here https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX/issues/1

perceptual hashes are completely broken due to false positives, like making a picture of a dog having the same hash as CSAM images. They are vulnerable to collision and have no preimage resistance, the most important feature. Because of that you can DOS conversations and people by sending dog images with the same hash as a CSAM: https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX/issues/1

Specifically the fact that you can have one hash on two images, for example. There are other ways to abuse the CSAM system, which is confirmed by several researchers (one, two). And Apple saying it's not a concern... only to pull back on these plans.