In this paper, we study the worst-case consequence of innovation-based tegrity attacks with side information in a remote state estimation enario where a sensor transmits its measurement to a remote estimator uipped with a false-data detector. If a malicious attacker is not only le to compromise the transmitted data packet but also able to measure e system state itself, the attack strategy can be designed based on e intercepted data, the sensing data, or alternatively the combined formation. Surprisingly, we show that launching attacks using the mbined information are not always optimal. First, we characterize the ealthiness constraints for different types of attack strategies to oid being noticed by the false-data detector. Then, we derive the olution of the remote estimation error covariance in the presence of tacks, based on which the worst-case attack policies are obtained by lving convex optimization problems. Furthermore, the closed-form pressions of the worst-case attacks are obtained for scalar systems d the attack consequences are compared with the existing work to termine which strategy is more critical in deteriorating system rformance. Simulation examples are provided to illustrate the alytical results.