VISIBLE: Video-Assisted Keystroke Inference from Tablet Backside Motion

Sun, Jing; Jin, Xiaocong; Chen, Yimin; Zhang, Jinxue; Zhang, Yanchao; Zhang, Rui

doi:10.14722/ndss.2016.23060

Cited by 51 publications

(51 citation statements)

References 32 publications

(37 reference statements)

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“…In this case, an attacker knows certain digits of a target PIN before launching inter-keystroke timing attacks (e.g., through other side channel attacks [18,75,54,37,65,59] or shoulder surfing attacks [60]). Hence, he/she can reduce the size of his/her timing dictionary.…”

Section: Known Digits Attacksmentioning

confidence: 99%

“…We further examine the scenario in which an attacker happens to know the values of certain digits of the target PIN before launching inter-keystroke timing attacks. It is reasonable to assume that an attacker may attain such knowledge about PIN digits due to the existence of many side-channel attacks (e.g., [18,75,54,37,65,59]) and shoulder surfing attacks [60] to the PIN entry. Unsurprisingly, the success rate of our attacks is significantly improved due to the shrink of timing dictionary in our attacks.…”

Section: Introductionmentioning

confidence: 99%

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When Human cognitive modeling meets PINs: User-independent inter-keystroke timing attacks

Liu

Deng

et al. 2019

Computers & Security

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This paper proposes the first user-independent inter-keystroke timing attacks on PINs. Our attack method is based on an interkeystroke timing dictionary built from a human cognitive model whose parameters can be determined by a small amount of training data on any users. Our attacks can thus be potentially launched in a large scale in real-world settings. We investigate inter-keystroke timing attacks in different online attack settings and evaluate their performance on PINs at different strength levels. Our experimental results show that the proposed attack performs significantly better than random guessing attacks. We further demonstrate that our attacks pose a serious threat to real-world applications and propose various ways to mitigate the threat.

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Section: Known Digits Attacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When Human cognitive modeling meets PINs: User-independent inter-keystroke timing attacks

Liu

Deng

et al. 2019

Computers & Security

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“…By observing the motion of the user's hand, the path of the finger across the screen can be reconstructed, which yields the typed text. In a similar attack, Sun et al [24] successfully reconstructed text typed on tablets by recording and analyzing the tablet's movements, rather than movements of the user's hands.…”

Section: Related Workmentioning

confidence: 99%

PILOT: Password and PIN information leakage from obfuscated typing videos1

Balagani

Cardaioli

Conti

et al. 2019

JCS

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This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters ( * or •) that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.Passwords and PINs are susceptible to shoulder surfing attacks [25] of which there are two main types: (1) input-based and (2) output-based. The former is more common; in it, the adversary observes an input device (keyboard or keypad) as the user enters a secret (password or PIN) and learns the key-presses. The latter

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“…Roth, et al [24] utilized video imaging to classify keystroke behavior, while Roth, et al [25] utilized the sound of a keyboard to achieve similar results. Further, Sun, et al [32] utilized the motion of the backside of a tablet computer to capture keystroke data.…”

Section: Accepted Manuscriptmentioning

confidence: 99%