Summary

This chapter has described the importance of having good data to ensure the security of ML applications. Often, the damage caused by modified or corrupted data is subtle and no one will actually notice it until it’s too late: an analysis is incorrect, a faulty recommendation causes financial harm, an assembly line may not operate correctly, a classification may fail to point out that a patient has cancer, or any number of other issues may occur. The focus of most data damage is causing the model to behave in a manner other than needed. The techniques in this chapter wil help you avoid – but not necessarily always prevent – data modification or corruption.

The hardest types of modification and corruption to detect and mitigate are those created by humans in most cases, which is why the human factor receives special treatment in this chapter. Modifications that are automated in nature have a recognizable pattern and most environmental causes are rare enough...