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Automated Teller Machines (ATMs) represent the most used system forwithdrawing cash. The European Central Bank reported more than 11 billion cashwithdrawals and loading/unloading transactions on the European ATMs in 2019.Although ATMs have undergone various technological evolutions, PersonalIdentification Numbers (PINs) are still the most common authentication methodfor these devices. Unfortunately, the PIN mechanism is vulnerable toshoulder-surfing attacks performed via hidden cameras installed near the ATM tocatch the PIN pad. To overcome this problem, people get used to covering thetyping hand with the other hand. While such users probably believe thisbehavior is safe enough to protect against mentioned attacks, there is no clearassessment of this countermeasure in the scientific literature.  This paper proposes a novel attack to reconstruct PINs entered by victimscovering the typing hand with the other hand. We consider the setting where theattacker can access an ATM PIN pad of the same brand/model as the target one.Afterward, the attacker uses that model to infer the digits pressed by thevictim while entering the PIN. Our attack owes its success to a carefullyselected deep learning architecture that can infer the PIN from the typing handposition and movements. We run a detailed experimental analysis including 58users. With our approach, we can guess 30% of the 5-digit PINs within threeattempts -- the ones usually allowed by ATM before blocking the card. We alsoconducted a survey with 78 users that managed to reach an accuracy of only7.92% on average for the same setting. Finally, we evaluate a shieldingcountermeasure that proved to be rather inefficient unless the whole keypad isshielded.

Hand Me Your PIN! Inferring ATM PINs of Users Typing with a Covered Hand