Driven by a wide range of essential applications, significant achievements have recently been made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf (COTS) WiFi infrastructures to infer human activities without the need for the subject to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti in this paper, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information (CSI) to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios. Specifically, PhaseAnti achieves a$ 96.5\%$recognition accuracy rate (RAR) on average in different CCI scenarios, which can improve up to a$ 16.7\%$RAR compared with the amplitude component in the presence of CCI. Furthermore, the recognition speed is 10.3 × faster than the state-of-the-art solution.