Audio Information Camouflage Detection for Social Networks

Abstract

Sending camouflaged audio information for fraud in social networks has become a new means of social networks attack. The hidden acoustic events in the audio scene play an important role in the detection of camouflaged audio information. Therefore, the application of machine learning methods to represent hidden information in audio streams has become a hot issue in the field of network security detection. This study proposes a heuristic mask for empirical mode decomposition (HM-EMD) method for extracting hidden features from audio streams. The method consists of two parts: First, it constructs heuristic mask signals related to the signal’s structure to solve the modal mixing problem in intrinsic mode function (IMF) and obtains a pure IMF related to the signal’s structure. Second, a series of hidden features in environment-oriented audio streams is constructed on the basis of the IMF. A machine learning method and hidden information features are subsequently used for audio stream scene classification. Experimental results show that the hidden information features of audio streams based on HM-EMD are better than the classical mel cepstrum coefficients (MFCC) under different classifiers. Moreover, the classification accuracy achieved with HM-EMD increases by 17.4 percentage points under the three-layer perceptron and by 1.3% under the depth model of TridentResNet. The hidden information features extracted by HM-EMD from audio streams revealed that the proposed method could effectively detect camouflaged audio information in social networks, which provides a new research idea for improving the security of social networks.

COVID-19 Rumor Detection on Social Networks Based on Content Information and User Response

“…Interactions on social networks could help better represent user profiles. More user responses can be seen as connections on social network and will provide richer information to describing an event from a more abundant perspective [34][35][36][37][38][39]. To further understand the effect of user responses on rumor detection, we compared the accuracy of a different group of Twitter and microblog posts with various responses within 24 h. Figure 5 shows the rumor detection accuracy improvements of a different group of Twitter and microblog posts with various responses tested on CR-LSTM-BE and CSN-BERT.…”