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.

Echo hiding is a steganographic technique with audio as carrier. Currently， the steganalysis methods for echo hiding mainly use the cepstral coefficients as handcrafted-features to realize classification. However， when the echo amplitude is low， the detection performance of these traditional methods is not high. Aiming at the low echo amplitude condition， a steganalysis method for echo hiding based on Convolutional Neural Network （CNN） was proposed. Firstly， Short-Time Fourier Transform （STFT） was used to extract the amplitude spectrum coefficient matrix as the shallow feature. Secondly， the deep feature was extracted by the designed CNN framework from the shallow feature. The network framework consisted of four convolutional blocks and three fully connected layers. Finally， the classification results were output by Softmax. The proposed method was steganographically evaluated on three classic echo hiding algorithms. Experimental results indicate that the detection rates of the proposed method under low echo amplitude are 98.62%， 98.53% and 93.20% respectively. Compared with the existing traditional handcrafted-features based methods and deep learning based methods， the proposed method has the detection performance improved by more than 10%.