A ResNet-based faked sample detection algorithm was proposed for the detection of faked samples in audio scenes with low faking cost and undetectable sound replacement. The Constant Q Cepstral Coefficient （CQCC） features of the audio were extracted firstly， then the input features were learnt by the Residual Network （ResNet） structure， by combining the multi-layer residual blocks of the network and feature normalization， the classification results were output finally. On TIMIT and Voicebank databases， the highest detection accuracy of the proposed algorithm can reach 100%， and the lowest false acceptance rate of the algorithm can reach 1.37%. In realistic scenes， the highest detection accuracy of this algorithm is up to 99.27% when detecting the audios recorded by three different recording devices with the background noise of the device and the audio of the original scene. Experimental results show that it is effective to use the CQCC features of audio to detect the scene replacement trace of audio.