VStego800K: Large-Scale Steganalysis Dataset for Streaming Voice

Xuan Xu, Shengnan Guo, Zhengyang Fang, Pengcheng Zhou, Zhongliang Yang, Linna Zhou

Abstract: In recent years, more and more steganographic methods based on streaming voice have appeared, which poses a great threat to the security of cyberspace. In this paper, in order to promote the development of streaming voice steganalysis technology, we construct and release a large-scale streaming voice steganalysis dataset called VStego800K. To truly reflect the needs of reality, we mainly follow three considerations when constructing the VStego800K dataset: large-scale, real-time, and diversity. The large-scale dataset allows researchers to fully explore the statistical distribution differences of streaming signals caused by steganography. Therefore, the proposed VStego800K dataset contains 814,592 streaming voice fragments. Among them, 764,592 samples (382,296 cover-stego pairs) are divided as the training set and the remaining 50,000 as testing set. The duration of all samples in the data set is uniformly cut to 1 s to encourage researchers to develop near real-time speech steganalysis algorithms. To ensure the diversity of the dataset, the collected voice signals are mixed with male and female as well as Chinese and English from different speakers. For each steganographic sample in VStego800K, we randomly use two typical streaming voice steganography algorithms, and randomly embed random bit with embedding rates of 10%–40%. We tested the performance of some latest steganalysis algorithms on VStego800K, with specific results and analysis details in the experimental part. We hope that the VStego800K dataset will further promote the development of universal voice steganalysis technology. The description of VStego800K and instructions will be released here: https://github.com/YangzlTHU/VStego800K.