Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries
This work addresses the vulnerability of deep learning networks to adversarial attacks, offering a more efficient defense solution for AI security applications, though it is incremental in nature.
The paper tackled the problem of generating transferable multi-step adversarial examples and introduced a novel defense method that enhances neural network resilience with only a single additional forward-pass, achieving comparable performance to existing defenses.
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually -- been rather difficult. In this work, we introduce a method for generating strong ad-versaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.