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The ASVspoof 2017 challenge aims to assess spoofing and countermeasures attack detection accuracy for automatic speaker verification. It has been proven that constant Q cepstral coefficients (CQCCs) processes speech in different frequencies with variable resolution and performs much better than traditional features. When coupled with a Gaussian mixture model (GMM), it is an excellently effective spoofing countermeasure. The baseline CQCC+GMM system considers short-term impacts while ignoring the whole influence of channel. In the meanwhile, dimension of the feature is relatively higher than the traditional feature and usually with a higher variance. This paper explores different features for ASVspoof 2017 challenge. The mean and variance of the CQCC features of an utterance is used as the representation of the whole utterance. Feature selection method is introduced to avoid high variance and overfitting for spoofing detection. Experimental results on ASVspoof 2017 dataset show that feature selection followed by Support Vector Machine (SVM) gets an improvement compared to the baseline. It is also shown that pitch feature contributes to the performance improvement, and it obtains a relative improvement of 37.39% over the baseline CQCC+GMM system.