The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision

Understand

Resource efficiency plays an important role for machine learning nowadays.

• The energy and decision latency are two critical aspects to ensure a sustainable and practical application.
• Unfortunately, the energy consumption and decision latency are not robust against adversaries.
• Researchers have recently demonstrated that attackers can compute and submit so-called sponge examples at inference time to increase the energy consumption and decision latency of neural networks.