Identifying Concealed Channels in Internet Streaming Media Communication

Due to the exponential increase in cyber/online crimes involving the use of steganography in streaming media, it is crucial to focus on studying steganalysis technology for the detection of covert communications. Several existing steganalysis methods rely on classifiers such as Support Vector Machines (SVM) and conventional statistical analysis. However, these methods suffer from high computational intensity and time consumption, making them unsuitable for real-time detection of steganography in streaming media. Additionally, some steganalysis techniques are specifically designed for certain codecs or depend on particular steganographic algorithms, limiting their practicality and universality. To address these challenges, this study devises five steganalysis algorithms tailored for Voice over Internet Protocol (VoIP) to detect covert channels in streaming media communications. The algorithms are as follows: ANOVA, derivative-based Mel Frequency Cepstrum Coefficient (DMFCC), improved Regular and Singular (RS) test, Mann–Whitney–Wilcoxon (M-W-W) test, and FFT-based Steganalysis. Experimental evaluations were performed on two sets of media streams — one without steganography and the other with steganography. The results obtained from these experiments reveal that the proposed VoIP steganalysis algorithms exhibit variations in terms of detection sensitivity, whether blind or targeted, online or offline, as well as the requirement for original samples.