Title
General Frame-Wise Steganalysis of Compressed Speech Based on Dual-Domain Representation and Intra-Frame Correlation Leaching
Abstract
Frame-wise steganalysis is of significance for active steganography defense. By frame-wise detection, we can accurately find the embedding position of secret information and destroy the covert channel further. However, there is currently no research specifically aiming at frame-wise steganalysis of low-bit-rate compressed speech. Besides, most of the existing steganalysis methods are specifically designed for a specific category of steganography methods. They are difficult to apply to practical scenarios where the steganography algorithms are uncertain. In this paper, a general frame-wise steganalysis method for low-bit-rate compressed speech is proposed. To extract rich feature from a speech frame, we propose a dual-domain representation, which conducts feature extraction both in the compressed domain and the decoded time domain. In addition, we propose an efficient steganalysis network named Stegaformer to leach the intra-frame correlation from the obtained representation to enable steganalysis. In Stegaformer, an adaptive local correlation enhancement module is introduced to effectively models the local characteristics, which compensates for the drawback of traditional Transformer-based models. Experimental results show that our method performs better than the existing steganalysis methods in detecting multiple steganography methods for a speech frame.
Year
DOI
Venue
2022
10.1109/TASLP.2022.3182276
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Keywords
DocType
Volume
Steganography, Feature extraction, Speech processing, Correlation, Speech coding, Filtering algorithms, Information filters, Compressed speech, dual-domain representation, intra-frame correlation, steganalysis, transformer
Journal
30
ISSN
Citations 
PageRank 
2329-9290
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Songbin Li1534.79
Jingang Wang200.34
Peng Liu31701171.49