On the Interaction of Compressibility and Adversarial Robustness
The study analyzed how different types of model compression, like neuron sparsity and low-rank approximation, affect adversarial robustness. It found that these compressions concentrate model sensitivity onto a few dominant directions in the data space, creating exploitable weaknesses for attackers. This reveals a fundamental trade-off: structured compression can introduce vulnerabilities.