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Data poisoningBoth bad actors and human error can cause data poisoning. This phenomenon occurs when bad, malicious, or inaccurate information is fed into an AI model. This can cause a load of issues, including the AI reaching incorrect conclusions, erroneous analysis of company data, and bad code being pushed that can cause bugs and other problems.

While this is immediately effective, the random perturbations introduce a disturbing texture that can obfuscate details in the original image. To counter this, we can make some smart choices on where and by how much to perturb our input image in an attempt to add some structure to our dither and preserve some of the lost detail.

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