  In this paper, we propose a game theoretical adversarial intervention
detection mechanism for reliable smart road signs. A future trend in
intelligent transportation systems is ``smart road signs" that incorporate
smart codes (e.g., visible at infrared) on their surface to provide more
detailed information to smart vehicles. Such smart codes make road sign
classification problem aligned with communication settings more than
conventional classification. This enables us to integrate well-established
results in communication theory, e.g., error-correction methods, into road sign
classification problem. Recently, vision-based road sign classification
algorithms have been shown to be vulnerable against (even) small scale
adversarial interventions that are imperceptible for humans. On the other hand,
smart codes constructed via error-correction methods can lead to robustness
against small scale intelligent or random perturbations on them. In the
recognition of smart road signs, however, humans are out of the loop since they
cannot see or interpret them. Therefore, there is no equivalent concept of
imperceptible perturbations in order to achieve a comparable performance with
humans. Robustness against small scale perturbations would not be sufficient
since the attacker can attack more aggressively without such a constraint.
Under a game theoretical solution concept, we seek to ensure certain measure of
guarantees against even the worst case (intelligent) attackers that can perturb
the signal even at large scale. We provide a randomized detection strategy
based on the distance between the decoder output and the received input, i.e.,
error rate. Finally, we examine the performance of the proposed scheme over
various scenarios.
