Download or read book Towards Robust Deep Neural Networks written by Andras Rozsa and published by . This book was released on 2018 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the greatest technological advancements of the 21st century has been the rise of machine learning. This thriving field of research already has a great impact on our lives and, considering research topics and the latest advancements, will continue to rapidly grow. In the last few years, the most powerful machine learning models have managed to reach or even surpass human level performance on various challenging tasks, including object or face recognition in photographs. Although we are capable of designing and training machine learning models that perform extremely well, the intriguing discovery of adversarial examples challenges our understanding of these models and raises questions about their real-world applications. That is, vulnerable machine learning models misclassify examples that are indistinguishable from correctly classified examples by human observers. Furthermore, in many cases a variety of machine learning models having different architectures and/or trained on different subsets of training data misclassify the same adversarial example formed by an imperceptibly small perturbation. In this dissertation, we mainly focus on adversarial examples and closely related research areas such as quantifying the quality of adversarial examples in terms of human perception, proposing algorithms for generating adversarial examples, and analyzing the cross-model generalization properties of such examples. We further explore the robustness of facial attribute recognition and biometric face recognition systems to adversarial perturbations, and also investigate how to alleviate the intriguing properties of machine learning models.