Towards Sustainable SecureML: Quantifying Carbon Footprint of
  Adversarial Machine Learning
Syed Mhamudul Hasan Abdur R. Shahid Ahmed Imteaj
The widespread adoption of machine learning (ML) across various industries
has raised sustainability concerns due to its substantial energy usage and
carbon emissions. This issue becomes more pressing in adversarial ML, which
focuses on enhancing model security against different network-based attacks.
Implementing defenses in ML systems often necessitates additional computational
resources and network security measures, exacerbating their environmental
impacts. In this paper, we pioneer the first investigation into adversarial
ML's carbon footprint, providing empirical evidence connecting greater model
robustness to higher emissions. Addressing the critical need to quantify this
trade-off, we introduce the Robustness Carbon Trade-off Index (RCTI). This
novel metric, inspired by economic elasticity principles, captures the
sensitivity of carbon emissions to changes in adversarial robustness. We
demonstrate the RCTI through an experiment involving evasion attacks, analyzing
the interplay between robustness against attacks, performance, and carbon
emissions.