  The widespread application of audio and video communication technology make
the compressed audio data flowing over the Internet, and make it become an
important carrier for covert communication. There are many steganographic
schemes emerged in the mainstream audio compression data, such as AAC and MP3,
followed by many steganalysis schemes. However, these steganalysis schemes are
only effective in the specific embedded domain. In this paper, a general
steganalysis scheme Spec-ResNet (Deep Residual Network of Spectrogram) is
proposed to detect the steganography schemes of different embedding domain for
AAC and MP3. The basic idea is that the steganographic modification of
different embedding domain will all introduce the change of the decoded audio
signal. In this paper, the spectrogram, which is the visual representation of
the spectrum of frequencies of audio signal, is adopted as the input of the
feature network to extract the universal features introduced by steganography
schemes; Deep Neural Network Spec-ResNet is well-designed to represent the
steganalysis feature; and the features extracted from different spectrogram
windows are combined to fully capture the steganalysis features. The experiment
results show that the proposed scheme has good detection accuracy and
generality. The proposed scheme has better detection accuracy for three
different AAC steganographic schemes and MP3Stego than the state-of-arts
steganalysis schemes which are based on traditional hand-crafted or CNN-based
feature. To the best of our knowledge, the audio steganalysis scheme based on
the spectrogram and deep residual network is first proposed in this paper. The
method proposed in this paper can be extended to the audio steganalysis of
other codec or audio forensics.
