# Download from HuggingFace (requires pip install huggingface_hub)
Another way to approach dithering is to analyse the input image in order to make informed decisions about how best to perturb pixel values prior to quantisation. Error-diffusion dithering does this by sequentially taking the quantisation error for the current pixel (the difference between the input value and the quantised value) and distributing it to surrounding pixels in variable proportions according to a diffusion kernel . The result is that input pixel values are perturbed just enough to compensate for the error introduced by previous pixels.
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There is no syscall surface to attack because the code never makes syscalls. Memory safety is enforced by the runtime. The linear memory is bounds-checked, the call stack is inaccessible, and control flow is type-checked. Modern runtimes add guard pages and memory zeroing between instances.