Bonferonni correction is relevant when you calculate multiple p-values. Most statistical tests are used with a p-value threshold of 5% to reject the null-hypothesis. But because you are repeatedly testing, the probability for false positives increases and that is why you need to decrease the threshold and make it harder, to obtain a p-value below that threshold to declare a significant result.
You typically use the Bonferroni correction when making general statements about a statistical relationship. You wouldn't use it for checking if a particular image shows illegal content. If you kept testing with your image classifier, your significance threshold would need to be continuously lowered and you would asymptotically reach zero.
No, I'm not referring to Bonferroni's correction, which obviously has nothing to do with what we're discussing. I'm sure I've heard this called Bonferroni's principle.
Edit: Yes, in the section 'Statistical Limits of Data Mining' in the book Mining of mMssive Data Sets it's called Bonferroni's principle.
You typically use the Bonferroni correction when making general statements about a statistical relationship. You wouldn't use it for checking if a particular image shows illegal content. If you kept testing with your image classifier, your significance threshold would need to be continuously lowered and you would asymptotically reach zero.
Relevant XKCD: 882