A Robust-Based Framework towards Resisting Adversarial Attack on Deep Learning Models

Adversarial attack is a type of attack executed by an attacker in other to confuse a deep learning model to falsely classify a wrong input data as the correct data. This attack is being executed in two ways. The first one is the Poisoning attack which is being generated during training of a deep learning model. And the second one is the Evasion attack. In the evasion assault, the assaults' is being done on the test dataset. An evasion assault happens when the computer network is taken care of an “adversarial model”, a painstakingly perturbed info that looks and feels precisely equivalent to its untampered duplicate to a human however that totally loses the classifier that. This system presents a robust based model towards the resistance of adversary assaults on deep learning models. The system presents two models using convolutional neural network algorithm. This model was trained on a Modified National Institute of Standards and Technology dataset (MNIST). An adversary (evasion) attacks was generated to in other to fools this models to misclassify result, therefore, seeing the wrong input data to be the right one. This adversarial examples was generated using a state-of-the-art library in python. The generated adversarial examples was being generated on the test data, in which the first model fails in resisting the attack, while the second model, which is our robust model resisted the adversarial attack on a good number of accuracy when tested for the first 100 images