Title: Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack. Abstract: Discovering adversarial examples has shaken our trust in the reliability of deep learning. Even though brilliant works have been devoted to understanding and fixing this vulnerability, fundamental questions (e.g. the mysterious generalization of adversarial examples across models and training sets) remain unanswered. This paper tests the hypothesis that it is not the neural networks failing in learning that causes adversarial vulnerability, but their different perception of the presented data. And therefore, adversarial examples should be semantic-sensitive signals which can provide us with an exceptional opening to understanding neural network learning. To investigate this hypothesis, I performed a gradient-based attack on fully connected feed-forward and convolutional neural networks, instructing them to minimally evolve controlled inputs into adversarial examples for all the classes of the MNIST and Fashion-MNIST datasets. Then I abstracted adversarial perturbations from these examples. The perturbations unveiled vivid and recurring visual structures, unique to each class and persistent over parameters of abstraction methods, model architectures, and training configurations. Furthermore, these patterns proved to be explainable and derivable from the corresponding dataset. This finding explains the generalizability of adversarial examples by, semantically, tying them to the datasets. In conclusion, this experiment not only resists interpretation of adversarial examples as deep learning failure but on the contrary, demystifies them in the form of supporting evidence for the authentic learning capacity of neural networks.