Multi-SAP Adversarial Defense for Deep Neural Networks

Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several L∞ attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several L∞ attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.