Coreml

In the last two posts (about perceptual hashes in the context of Chat Control and about their adversarial collisions), we explored how 40 lines of Python can solve a problem that the industry often deploys at a global scale. Perceptual hashes are the canonical example of a cheap deterministic layer: no models, no GPUs, no heavyweight dependencies. Hashes handle duplicate and similarity detection. But in a real-world image processing pipeline, there’s work that classical algorithms can’t do: detecting bounding boxes of arbitrary watermarks, filling gaps with inpainting that respects visual context, classifying whether a face is of an adult, or extracting semantic attributes. This work requires large neural networks. ...