Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning. (arXiv:2206.07406v1 [cs.LG])

The prevalence and success of Deep Neural Network (DNN) applications in
recent years have motivated research on DNN compression, such as pruning and
quantization. These techniques accelerate model inference, reduce power
consumption, and reduce the size and complexity of the hardware necessary to
run DNNs, all with little to no loss in accuracy. However, since DNNs are
vulnerable to adversarial inputs, it is important to consider the relationship
between compression and adversarial robustness. In this work, we investigate
the adversarial …

adversarial attacks compression greedy hardening lg network