TREND: Transferability based Robust ENsemble Design
This work addresses the vulnerability of deep learning models to adversarial attacks, which is a critical issue for their safe deployment in practical applications, though it is incremental in nature.
The paper tackles the problem of adversarial example transferability in deep learning models by systematically studying factors like architecture and quantization, and proposes a new ensemble attack and design methodology that improves adversarial robustness.
Deep Learning models hold state-of-the-art performance in many fields, but their vulnerability to adversarial examples poses threat to their ubiquitous deployment in practical settings. Additionally, adversarial inputs generated on one classifier have been shown to transfer to other classifiers trained on similar data, which makes the attacks possible even if model parameters are not revealed to the adversary. This property of transferability has not yet been systematically studied, leading to a gap in our understanding of robustness of neural networks to adversarial inputs. In this work, we study the effect of network architecture, initialization, optimizer, input, weight and activation quantization on transferability of adversarial samples. We also study the effect of different attacks on transferability. Our experiments reveal that transferability is significantly hampered by input quantization and architectural mismatch between source and target, is unaffected by initialization but the choice of optimizer turns out to be critical. We observe that transferability is architecture-dependent for both weight and activation quantized models. To quantify transferability, we use simple metric and demonstrate the utility of the metric in designing a methodology to build ensembles with improved adversarial robustness. When attacking ensembles we observe that "gradient domination" by a single ensemble member model hampers existing attacks. To combat this we propose a new state-of-the-art ensemble attack. We compare the proposed attack with existing attack techniques to show its effectiveness. Finally, we show that an ensemble consisting of carefully chosen diverse networks achieves better adversarial robustness than would otherwise be possible with a single network.