Recently, it has been shown that adversaries can reconstruct images from SIFT features through reverse attacks. However, the images reconstructed by existing reverse attack methods suffer from information loss and are unable to sufficiently reveal the private contents of the original images. In this paper, a two-stage deep reverse attack model called Coarse-to-Fine Generative Adversarial Network (CFGAN) is proposed to more deeply explore the information in SIFT features and further demonstrate the risk of privacy leakage associated with SIFT features. Specifically, the proposed model consists of two sub-networks, namely coarse net and fine net. The coarse net is developed to restore coarse images using SIFT features, while the fine net is responsible for refining the coarse images to obtain better reconstruction results. To effectively leverage the information contained in SIFT features, an efficient fusion strategy based on the AdaIN operation is designed in the fine net. Additionally, we introduce a new loss function called sift loss that enhances the color fidelity of reconstructed images. Extensive experiments conducted on various datasets verify that the proposed CFGAN performs favorably against state-of-the-art methods. The reconstructed images exhibit better visual quality, less texture distortion, and higher color fidelity. Source code is available at https://github.com/HITLiXincodes/CFGAN.