NEUROCRYPT: Coercion-Resistant Implicit Memory Authentication (Student Abstract)


  • Ritul Satish Ashoka University
  • Niranjan Rajesh Ashoka University
  • Argha Chakrabarty Ashoka University
  • Aditi Jain Ashoka University
  • Sristi Bafna Ashoka University
  • Arup Mondal Ashoka University
  • Debayan Gupta Ashoka University



Authentication System, Coercion Attack, Rubber-Hose Attack, Implicit Memory, Implicit Learning, Serial Interception Sequence Learning, Computer-Human Modalities


Overcoming the threat of coercion attacks in a cryptographic system has been a top priority for system designers since the birth of cyber-security. One way to overcome such a threat is to leverage implicit memory to construct a defense against rubber-hose attacks where the users themselves do not possess conscious knowledge of the trained password. We propose NeuroCrypt, a coercion-resistant authentication system that uses an improved version of the Serial Interception Sequence Learning task, employing additional auditory and haptic modalities backed by concepts borrowed from cognitive psychology. We carefully modify the visual stimuli as well as add auditory and haptic stimuli to improve the implicit learning process, resulting in faster training and longer retention. Moreover, our improvements guarantee that explicit recognition of the trained passwords remains suppressed.




How to Cite

Satish, R., Rajesh, N., Chakrabarty, A., Jain, A., Bafna, S., Mondal, A., & Gupta, D. (2022). NEUROCRYPT: Coercion-Resistant Implicit Memory Authentication (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13041-13042.