Building Robust Deep Neural Networks for Road Sign Detection

With the availability of more computational resources and abundance of data, there has been a huge resurgence of using deep neural networks to do object recognition and classification but several machine learning models, including state-of-the-art deep neural networks, consistently misclassify adversarial examples, which are inputs formed by applying small, but intentionally engineered, worst-case perturbations to input images. These perturbations are indiscernible for humans, but they can make deep neural networks to make wrong classifications with very high confidence. The problem becomes more concerning with the advent of self-driving cars which does automatic detection and classification of road signs to do path planning, adjusting speed or driving behaviors. If the Convolutional Neural Network which detects road signs in a self-driving car is fed with adversarial inputs, even though it is obvious for a human to classify it correctly, the network may make an egregious misclassification of that road sign. This can result in self-driving cars making erroneous decisions. In this work, ways to create adversarial examples from road sign images are explored in order to use them to fool the state-of-the-art neural networks and an effort to build more robust neural networks to be resilient against these attacks is made. In Section 2, some of the previous work that has been done related to adversarial examples is addressed. Explanations of the methods that were used to craft adversarial examples and the ways used to build more robust neural networks to be resilient against adversarial samples are presented in Section 3. The dataset used and the data augmentation processes are also described in 3. Experimental results are shown in Section 4 and finally, further discussions on the weakness of this work as well as the possible future extensions of this work are discussed in Section 5. Finally, the scope of the work is concluded in Section 6.