Acoustic Side-Channel Attacks on Printers

We examine the problem of acoustic emanations of printers. We present a novel attack that recovers what a dotmatrix printer processing English text is printing based on a record of the sound it makes, if the microphone is close enough to the printer. In our experiments, the attack recovers up to 72 % of printed words, and up to 95 % if we assume contextual knowledge about the text, with a microphone at a distance of 10cm from the printer. After an upfront training phase, the attack is fully automated and uses a combination of machine learning, audio processing, and speech recognition techniques, including spectrum features, Hidden Markov Models and linear classification; moreover, it allows for feedbackbased incremental learning. We evaluate the effectiveness of countermeasures, and we describe how we successfully mounted the attack in-field (with appropriate privacy protections) in a doctor’s practice to recover the content of medical prescriptions.