Abstract
Recently, deep learning based audio steganalysis methods have demonstrated superior performance in detecting the conventional audio steganography, which poses great chanllegnes to the conveiontional audio steganography. In this work, observed that the neural network can easily be deceived by specially perturbed inputs, i.e., adversarial examples, we propose an imperceptible audio steganography method based on psychoacoustic model. Specifically, we first add perturbation on the stego audio for constructing noise stego audio, which is delivered to the trained steganalyzer for misclassification. The perturbation is optimized in the adversarial process, aiming to seek an optimal perturbation that guarantee the imperceptibility and undetectability of stego audio. Further consider that the difficulty to optimize the threshold loss function using gradient back-progagation, we adopt two-stage optimization strategy to minimize the loss function. The first stage attempts to find a suitable perturbation to deceive the steganalyzer. The second stage concentrates on further optimizing the perturbation to make the stego imperceptible. For the practical steganography, the optimal perturbation obtained from the adversarial attack process is added on the original cover audio to construct the adversarial cover audio. Then one can use information embedding algorithm to embed the secret message on the adversarial cover to generate stego audio. Extensive experiments show that the proposed method can generate the adversarial cover audio with high perceptual quality and the undetectability performance outperforms the conventional audio steganography schemes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Bender W, Gruhl D, Morimoto N, Lu A (1996) Techniques for data hiding. IBM Syst J 35(3.4):313–336
Bosi M, Goldberg RE (2002) Introduction to digital audio coding and standards. Springer, Berlin
Chen B, Luo W, Li H (2018) Audio steganalysis with convolutional neural network. In: ACM workshop on information hiding and multimedia security, pp 85–90
Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inform Forensics Secur 6(3):920–935
Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inform Forensics Secur 7(3):868–882
Garofolo JS, Lamel LF, Fisher WM, Fiscus JG, Pallett DS (1993) DARPA TIMIT Acoustic-phonetic continous speech corpus CD-ROM. NIST speech disc, 1–1.1
Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv:1412.6572
Hayes J, Danezisv G (2017) Generating steganographic images via adversarial training. arXiv:1703.00371
Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. In: IEEE International workshop on information forensics and security, pp 234–239
Holub V, Fridrich J (2013) Digital image steganography using universal distortion. In: ACM Workshop on information hiding and multimedia security, pp 59–68
Holub V, Fridrich J (2013) Random projections of residuals for digital image steganalysis. IEEE Trans Inform Forensics Secur 8(12):1996–2006
Kurakin A, Goodfellow I, Bengio S (2016) Adversarial examples in the physical world. arXiv:1607.02533
Li B, Tan S, Wang M, Huang J (2014) Investigation on cost assignment in spatial image steganography. IEEE Trans Inform Forensics Secur 9 (8):1264–1277
Lin Y, Abdulla WH (2015) Principles of Psychoacoustics. Springer, Berlin
Lin Y, Wang R, Yan D, Dong L, Zhang X (2019) Audio steganalysis with improved convolutional neural network. In: ACM workshop on information hiding and multimedia security, pp 210–215
Luo W, Li H, Yan Q, Yang R, Huang J (2018) Improved audio steganalytic feature and its applications in audio forensics. ACM Transactions on Multimedia Computing Communications, and Applications 14(2):1–14
Luo W, Zhang Y, Li H (2017) Adaptive audio steganography based on advanced audio coding and syndrome-trellis coding. In: International workshop on digital watermarking, pp 177–186
Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2018) Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083
Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inform Forensics Secur 5(2):215–224
Pevnỳ T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: International workshop on information hiding, pp 161–177
Priority areas (2002) Advanced utilization of multimedia to promote higher education reform speech database - English speech database read by Japanese students
Rix AW, Beerends JG, Hollier MP, Hekstra AP (2001) Perceptual evaluation of speech quality- a new method for speech quality assessment of telephone networks and codecs. In: International conference on acoustics, speech, and signal processing, pp 749–752
Sharp T (2001) An implementation of key-based digital signal steganography. In: International workshop on information hiding, pp 13–26
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition, pp 1–9
Tang W, Li B, Barni M, Li J, Huang J (2020) An automatic cost learning framework for image steganography using deep reinforcement learning. IEEE Trans Inform Forensics Secur (16):952–967
Tang W, Li B, Tan S, Barni M, Huang J (2019) CNN-Based adversarial embedding for image steganography. IEEE Trans Inform Forensics Secur 14(8):2074–2087
Tang W, Tan S, Li B, Huang J (2017) Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process Lett 24(10):1547–1551
Volkhonskiy D, Nazarov I, Burnaev E (2017) Steganographic generative adversarial networks. arXiv:1703.05502
Wu S, Zhong S, Liu Y (2018) Deep residual learning for image steganalysis. Multimed Tools Appl 77(9):10437–10453
Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708–712
Zhang Y, Zhang W, Chen K, Liu J, Liu Y, Yu N (2018) Adversarial examples against deep neural network based steganalysis. In: ACM workshop on information hiding and multimedia security, pp 67–72
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 62171244), Ningbo Natural Science Foundation-Young Doctoral Innovation Research Project (Grant No. 2022J080), and Major Special Projects of “Unveiling the List and Taking the Lead” and “Scientific and Technological Innovation 2025” in Ningbo (Grant No. 2022Z074).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, L., Wang, R., Dong, L. et al. Imperceptible adversarial audio steganography based on psychoacoustic model. Multimed Tools Appl 82, 26451–26463 (2023). https://doi.org/10.1007/s11042-023-14772-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14772-9