There has to be some kind of DNN research on using methods such as these to enhance accuracy of DNNs by using the antagonist examples as input. You could just

There has to be some kind of DNN research on using methods such as these to enhance accuracy of DNNs by using the antagonist examples as input. You could just take your existing labeled data and add light-medium noise to all your data (easiest with images?) and thereby get a lot more training data less prone to overfitting. It's so obvious to me so someone must have thought of it already. So my guess is that these kinds of easy antagonist examples won't be a thing for very long.