Separable Convolution Network With Dual-Stream Pyramid Enhanced Strategy for Speech Steganalysis-Reference-Cited by-同舟云学术

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Separable Convolution Network With Dual-Stream Pyramid Enhanced Strategy for Speech Steganalysis

Published:2023 Issue: Volume:18 Page:2737-2750
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Qiu Yiqin¹^ORCID,Tian Hui¹^ORCID,Li Haizhou²^ORCID,Chang Chin-Chen³^ORCID,Vasilakos Athanasios V.⁴^ORCID

Affiliation:

1. College of Computer Science and Technology, National Huaqiao University, Xiamen, China

2. School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China

3. Department of Information and Computer Science, Feng Chia University, Taichung, Taiwan

4. Center for AI Research, University of Agder, Grimstad, Norway

Funder

National Natural Science Foundation of China

Major Science and Technology Project of Xiamen

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10107475.pdf?arnumber=10107475

Reference69 articles.

1. An AMR adaptive steganographic scheme based on the pitch delay of unvoiced speech

2. Convolutional sequence to sequence learning;gehring;Proc 34th Int Conf Mach Learn,2017

3. Steganography Integration Into a Low-Bit Rate Speech Codec

4. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst (NIPS),2017

5. A novel steganographic method for algebraic-code-excited-linear-prediction speech streams based on fractional pitch delay search

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions;Neurocomputing;2024-05

2. FedSpy: A Secure Collaborative Speech Steganalysis Framework Based on Federated Learning;Electronics;2023-06-28

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