Benchmarking Adversarial Robustness of Compressed Deep Learning Models
Brijesh Vora Kartik Patwari Syed Mahbub Hafiz Zubair Shafiq Chen-Nee Chuah
The increasing size of Deep Neural Networks (DNNs) poses a pressing need for
model compression, particularly when employed on resource constrained devices.
Concurrently, the susceptibility of DNNs to adversarial attacks presents
another significant hurdle. Despite substantial research on both model
compression and adversarial robustness, their joint examination remains
underexplored. Our study bridges this gap, seeking to understand the effect of
adversarial inputs crafted for base models on their pruned versions. To examine
this relationship, we have developed a comprehensive benchmark across diverse
adversarial attacks and popular DNN models. We uniquely focus on models not
previously exposed to adversarial training and apply pruning schemes optimized
for accuracy and performance. Our findings reveal that while the benefits of
pruning enhanced generalizability, compression, and faster inference times are
preserved, adversarial robustness remains comparable to the base model. This
suggests that model compression while offering its unique advantages, does not
undermine adversarial robustness.