SteganoGAN: High Capacity Image Steganography with GANs

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 for hiding arbitrary binary data in images using generative adversarial networks which allow us to optimize the perceptual quality of the images produced by our model. 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 this https URL.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[18]  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.

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

[20]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

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

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

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

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

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

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

[27]  Yann LeCun,et al.  Energy-based Generative Adversarial Networks , 2016, ICLR.

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

[29]  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).

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

[31]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

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

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