The goal of Image Manipulation Detection (IMD) is to identify and locate manipulated regions within images. Recent approaches have primarily designed sophisticated neural networks to capture high-frequency information for IMD tasks. However, these methods often overemphasize high-frequency information while overlooking the important role of low-frequency information in IMD tasks. To address this issue, we propose a Triple-Branch Frequency-Aware Network (TFNet), which includes an Information Separation Module (ISM), a Main Steam Branch (MSB), a Low-Frequency Learning Branch (LFL), a High-Frequency Learning Branch (HFL), and an Adaptive Aggregate Module (AAM) within a unified framework. Specifically, TFNet initially employs FSM to separate the manipulated image into RGB, high-frequency, and low-frequency components. Then, MSB, LFB, and HFB take the above components as input to learn the distinct features. Furthermore, the HFB is supervised with the boundary information to encourage the network to focus on the high-frequency information. Finally, the outputs of MSB, LFB, and HFB are sent to the ABM to adaptively aggregate features learned from the MSB, LFB, and HFB. Experiments on the CASIA, NIST, and Coverage datasets demonstrate the effectiveness of our TFNet.