Energy-latency attacks via sponge poisoning

Antonio Emanuele Cinà, Ambra Demontis, Battista Biggio, Fabio Roli, Marcello Pelillo

Abstract: Highlights•We propose the first poisoning attack to increase energy consumption in DNNs while preserving their prediction accuracy.•We formulate a novel objective function to target energy consumption in Hardware ASIC accelerators.•We inspect the model activations of the models to detect the most vulnerable layers against sponge poisoning attacks.•We show that the proposed attack can be adapted to avoid violating specific energy consumption requirements.•We show how to repair models targeted by sponge attacks, revealing an alternative path toward building energy-saving DNNs.