SteganoGAN: Pushing the Limits of Image Steganography

Image steganography is a procedure for hiding messages inside pictures. While other techniques such as cryptography aim to prevent adversaries from reading the secret message, steganography aims to hide the presence of the message itself. In this paper, we propose a novel technique based on generative adversarial networks for hiding arbitrary binary data in images. We show that our approach achieves state-of-the-art payloads of 4.4 bits per pixel, evades detection by steganalysis tools, and is effective on images from multiple datasets. To enable fair comparisons, we have released an open source library that is available online at https://github.com/DAI-Lab/SteganoGAN.

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

[2]  Jiangqun Ni,et al.  Deep Learning Hierarchical Representations for Image Steganalysis , 2017, IEEE Transactions on Information Forensics and Security.

[3]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

[6]  George Danezis,et al.  Generating steganographic images via adversarial training , 2017, NIPS.

[7]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[8]  Sorina Dumitrescu,et al.  Detection of LSB Steganography via Sample Pair Analysis , 2002, Information Hiding.

[9]  S. Katzenbeisser,et al.  A survey of steganographic techniques , .

[10]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[11]  Konrad Schindler,et al.  Learning by Tracking: Siamese CNN for Robust Target Association , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  George Ghinea,et al.  Stego image quality and the reliability of PSNR , 2010, 2010 2nd International Conference on Image Processing Theory, Tools and Applications.

[14]  Jessica J. Fridrich,et al.  Reliable detection of LSB steganography in color and grayscale images , 2001, MM&Sec '01.

[15]  Bin Li,et al.  A new cost function for spatial image steganography , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[16]  Sunanda Mitra,et al.  Secure transmission of medical records using high capacity steganography , 2004, Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems.

[17]  Ainuddin Wahid Abdul Wahab,et al.  Image steganography in spatial domain: A survey , 2018, Signal Process. Image Commun..

[18]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[19]  Yann LeCun,et al.  Energy-based Generative Adversarial Network , 2016, ICLR.

[20]  David Berthelot,et al.  BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.

[21]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Maura Conway,et al.  Code wars: Steganography, signals intelligence, and terrorism , 2003 .

[23]  Nasir D. Memon,et al.  On steganalysis of random LSB embedding in continuous-tone images , 2002, Proceedings. International Conference on Image Processing.

[24]  F. Moore,et al.  Polynomial Codes Over Certain Finite Fields , 2017 .

[25]  S. Uma Maheswari,et al.  Frequency domain QR code based image steganography using Fresnelet transform , 2015 .

[26]  Andreas Pfitzmann,et al.  Attacks on Steganographic Systems , 1999, Information Hiding.

[27]  Jessica J. Fridrich,et al.  Universal distortion function for steganography in an arbitrary domain , 2014, EURASIP Journal on Information Security.

[28]  Hideki Noda,et al.  A Model of Digital Contents Access Control System Using Steganographic Information Hiding Scheme , 2006, EJC.

[29]  Benedikt Boehm,et al.  StegExpose - A Tool for Detecting LSB Steganography , 2014, ArXiv.

[30]  Bin Li,et al.  Automatic Steganographic Distortion Learning Using a Generative Adversarial Network , 2017, IEEE Signal Processing Letters.

[31]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

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

[33]  Hao Li,et al.  High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[35]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Kevin Curran,et al.  An overview of steganography techniques applied to the protection of biometric data , 2017, Multimedia Tools and Applications.

[37]  Li Fei-Fei,et al.  HiDDeN: Hiding Data With Deep Networks , 2018, ECCV.

[38]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  John E. Hopcroft,et al.  Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

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

[42]  Eirikur Agustsson,et al.  NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[43]  Yang Yang,et al.  StegNet: Mega Image Steganography Capacity with Deep Convolutional Network , 2018, Future Internet.