Deep Residual Neural Networks for Image in Audio Steganography (Workshop Paper)

Steganography is the art of hiding a secret message inside a publicly visible carrier message. Ideally, it is done without modifying the carrier, and with minimal loss of information in the secret message. Recently, various deep learning based approaches to steganography have been applied to different message types. We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss. To achieve this, we train three neural networks; an encoding network to hide the message in the carrier, a decoding network to reconstruct the message from the carrier and an additional image enhancer network to further improve the reconstructed message. We also discuss future improvements to the algorithm proposed.

[1]  Ramit Sawhney,et al.  #YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media , 2019, ACL.

[2]  Fatiha Djebbar,et al.  Comparative study of digital audio steganography techniques , 2012, EURASIP J. Audio Speech Music. Process..

[3]  Shumeet Baluja,et al.  Hiding Images in Plain Sight: Deep Steganography , 2017, NIPS.

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  Ramit Sawhney,et al.  SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media , 2019, NAACL.

[6]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[7]  Yann Dauphin,et al.  Language Modeling with Gated Convolutional Networks , 2016, ICML.

[8]  Ramit Sawhney,et al.  Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment , 2020, AAAI.

[9]  Dr. med. Rajiv Shah,et al.  Multimodal Analysis of User-Generated Multimedia Content , 2017, Socio-Affective Computing.

[10]  Jessica J. Fridrich,et al.  Designing steganographic distortion using directional filters , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[11]  Ravi Shankar,et al.  A Comparative Study of Transfer Functions in Binary Evolutionary Algorithms for Single Objective Optimization , 2018, DCAI.

[12]  Bhiksha Raj,et al.  Hide and Speak: Towards Deep Neural Networks for Speech Steganography , 2019, INTERSPEECH.

[13]  Stephen Marshall,et al.  Activation Functions: Comparison of trends in Practice and Research for Deep Learning , 2018, ArXiv.

[14]  Ramit Sawhney,et al.  Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment , 2019, NAACL.

[15]  Abien Fred Agarap.  Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.

[16]  Puneet Mathur,et al.  #MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement , 2020, ICWSM.

[17]  Omaima N. A. AL-Allaf,et al.  Hiding an Image inside another Image using Variable-Rate Steganography , 2013 .

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[19]  Jiwu Huang,et al.  Robust Image Watermarking Based on Multiband Wavelets and Empirical Mode Decomposition , 2007, IEEE Transactions on Image Processing.

[20]  Roger Zimmermann,et al.  Multimodal Multi-Task Financial Risk Forecasting , 2020, ACM Multimedia.

[21]  Ramit Sawhney,et al.  #suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter , 2019, CIKM.

[22]  Tomás Pevný,et al.  Using High-Dimensional Image Models to Perform Highly Undetectable Steganography , 2010, Information Hiding.

[23]  Jonathan G. Fiscus,et al.  DARPA TIMIT:: acoustic-phonetic continuous speech corpus CD-ROM, NIST speech disc 1-1.1 , 1993 .

[24]  Jan H. P. Eloff,et al.  An overview of image steganography , 2005, ISSA.