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Neural style transfer for image steganography and destylization with supervised image to image translation

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Abstract

Today, a lot of information is being shared electronically in a way or another. Despite the advancements in technology used for data transfer, the reliable transmission of sensitive data is still a major challenge that need to be addressed. In this paper, we propose a steganographic technique to generate a stego image using the Neural Style Transfer (NST) algorithm that maintain the perceptual quality of the stego image with maximum capacity payload. Along with this, we propose to recover the secret content from generated stego image with minimal distortion. So, to recover the secret image from the generated stego image, destylization is performed using conditional Generative Adversarial Networks (cGANs). The proposed destyling GAN is forced to learn the embedded secret information using a loss function that learns the same representation as in the embedding NST algorithm. This whole framework of embedding and extraction of secret image is evaluated for Imagenet dataset and later tested on the PASCAL VOC12 dataset. The algorithm outperforms in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Visual Information Fidelity (VIF) with 44.175 dB, 0.9958, and 0.954 respectively for the Imagenet dataset. Also, the proposed algorithm is more robust against StegExpose with 0.529, area under the curve (AUC).

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References

  1. Baagyere EY, Agbedemnab PAN, Qin Z, Daabo MI, Qin Z (2020) A multi-layered data encryption and decryption scheme based on genetic algorithm and residual numbers. IEEE Access 8:100438–100447

    Article  Google Scholar 

  2. Baluja S (2017) Hiding images in plain sight: Deep steganography. In: Advances in neural information processing systems, pp 2069–2079

  3. Baluja S (2019) Hiding images in plain sight: Deep steganography. Adv Neural Inf Proc Syst, vol 30

  4. Bhattacharyya S, Khan A, Nandi A, Dasmalakar A, Roy S, Sanyal G (2011) Pixel mapping method (pmm) based bit plane complexity segmentation (bpcs) steganography. In: 2011 World congress on information and communication technologies, pp 36–41

  5. Biradar RL, Umashetty A (2016) A survey paper on steganography techniques, vol 4

  6. Boehm B Stegexpose-a tool for detecting lsb steganography. arXiv:1410.6656

  7. Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) Digital image steganography: Survey and analysis of current methods. Signal Process 90 (3):727–752

    Article  MATH  Google Scholar 

  8. Chen B, Luo W, Zheng P, Huang J (2020) Universal stego post-processing for enhancing image steganography. Journal of Information Security and Applications 55:102664

    Article  Google Scholar 

  9. Chen HY, Fang I, Cheng CM, Chiu WC, et al. (2020) Self-contained stylization via steganography for reverse and serial style transfer. In: The IEEE winter conference on applications of computer vision, pp 2163–2171

  10. Chu C, Zhmoginov A, Sandler M (2017) Cyclegan, a master of steganography. arXiv:1712.02950

  11. Duan X, Jia K, Li B, Guo D, Zhang E, Qin C (2019) Reversible image steganography scheme based on a u-net structure. IEEE Access 7:9314–9323

    Article  Google Scholar 

  12. Duan X, Nao L, Mengxiao G, Yue D, Xie Z, Ma Y, Qin C (2020) High-capacity image steganography based on improved fc-densenet. IEEE Access 8:170174–170182

    Article  Google Scholar 

  13. Dumitrescu S, Wu X, Memon N (2002) On steganalysis of random lsb embedding in continuous-tone images. In: Proceedings International conference on image processing, vol. 3, pp 641–644. IEEE

  14. Dumitrescu S, Wu X, Wang Z (2002) Detection of lsb steganography via sample pair analysis. In: International workshop on information hiding, pp 355–372. Springer

  15. Everingham M, Winn J (2012) The PASCAL visual object classes challenge 2012 (VOC2012) development kit. Pattern Anal Stat Model Comput Learn, Tech Rep 2007: 1–45

  16. Fridrich J, Goljan M, Du R (2001) Reliable detection of lsb steganography in color and grayscale images. In: Proceedings of the 2001 workshop on Multimedia and security: new challenges, pp 27–30

  17. Garg M, Ubhi JS, Aggarwal A (2019) Steganography and its advancements in spatial domain. EasyChair

  18. Garg M, Ubhi JS, Aggarwal A (2021) Deep learning for obstacle avoidance in autonomous driving. In: Autonomous driving and advanced driver-assistance systems (ADAS), pp 233–246. CRC press

  19. Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. arXiv:1508.06576

  20. Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414–2423

  21. Gatys LA, Ecker AS, Bethge M, Hertzmann A, Shechtman E (2017) Controlling perceptual factors in neural style transfer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3985–3993

  22. Ghosh S, Ghosh S, Kumar P, Scheme E, Roy PP (2021) A novel spatio-temporal siamese network for 3d signature recognition. Pattern Recogn Lett 144:13–20

    Article  Google Scholar 

  23. Hayes J, Danezis G (2017) Generating steganographic images via adversarial training. In: Advances in neural information processing systems, pp 1954–1963

  24. Hu D, Wang L, Jiang W, Zheng S, Li B (2018) A novel image steganography method via deep convolutional generative adversarial networks. IEEE Access 6:38303–38314

    Article  Google Scholar 

  25. Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE international conference on computer vision, pp 1501–1510

  26. Jiang D, Li G, Sun Y, Kong J, Tao B (2019) Gesture recognition based on skeletonization algorithm and cnn with asl database. Multimedia Tools and Applications 78(21):29953–29970

    Article  Google Scholar 

  27. Jiang D, Li G, Sun Y, Kong J, Tao B, Chen D (2019) Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG. Pers Ubiquit Comput, pp. 1–10. Springer

  28. Jiang D, Zheng Z, Li G, Sun Y, Kong J, Jiang G, et al. (2019) Gesture recognition based on binocular vision. Clust Comput 22(6):13261–13271

    Article  Google Scholar 

  29. Kadhim IJ, Premaratne P, Vial PJ (2020) High capacity adaptive image steganography with cover region selection using dual-tree complex wavelet transform. Cogn Syst Res 60:20–32

    Article  Google Scholar 

  30. Ke Y, Zhang M.q., Liu J, Su Tt, Yang Xy (2019) Generative steganography with kerckhoffs’ principle. Multimed Tools Appl 78(10):13805–13818

    Article  Google Scholar 

  31. Khatun A, Haque MR, Basri R, Uddin MS, et al. (2020) Single image dehazing: an analysis on generative adversarial network. Jl Comput Commun 8(04):127

    Article  Google Scholar 

  32. Kumar P, Scheme E (2021) A deep spatio-temporal model for eeg-based imagined speech recognition. In: ICASSP 2021-2021 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 995–999. IEEE

  33. Laskar SA, Hemachandran K (2013) Steganography based on random pixel selection for efficient data hiding. Jl Comput Commun 4(2):31–44

    Google Scholar 

  34. Li G, Jiang D, Zhou Y, Jiang G, Kong J, Manogaran G (2019) Human lesion detection method based on image information and brain signal. IEEE Access 7:11533–11542

    Article  Google Scholar 

  35. Li Y, Fang C, Yang J, Wang Z, Lu X, Yang MH (2017) Universal style transfer via feature transforms. In: Advances in neural information processing systems, pp 386–396

  36. Li Y, Wang N, Liu J, Hou X (2017) Demystifying neural style transfer. arXiv:1701.01036

  37. Lin Z, Huang Y, Wang J (2018) Rnn-sm: Fast steganalysis of voip streams using recurrent neural network. IEEE Transactions on Information Forensics and Security 13(7):1854–1868

    Article  Google Scholar 

  38. Mallika, Ubhi JS, Aggarwal A (2022) Neural style transfer for image within images and conditional gans for destylization. J Vis Commun Image Represent 85:103483. https://doi.org/10.1016/j.jvcir.2022.103483

    Article  Google Scholar 

  39. Meng R, Rice SG, Wang J, Sun X (2018) A fusion steganographic algorithm based on faster r-cnn. Computers, Materials & Continua 55(1):1–16

    Google Scholar 

  40. Noda H, Niimi M, Kawaguchi E (2006) High-performance jpeg steganography using quantization index modulation in dct domain. Pattern Recogn Lett 27(5):455–461. https://doi.org/10.1016/j.patrec.2005.09.008

    Article  Google Scholar 

  41. Olatunji J, Redding G, Rowe C, East A (2020) Reconstruction of kiwifruit fruit geometry using a cgan trained on a synthetic dataset. Comput Electron Agric 177:105699

    Article  Google Scholar 

  42. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434

  43. Rahim R, Nadeem S, et al. (2018) End-to-end trained cnn encoder-decoder networks for image steganography. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 0–0

  44. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis (IJCV) 115 (3):211–252. https://doi.org/10.1007/s11263-015-0816-y

    Article  Google Scholar 

  45. Setiadi DRIM (2019) Payload enhancement on least significant bit image steganography using edge area dilation. Int J Electron Telecommun 65

  46. Sharifzadeh M, Aloraini M, Schonfeld D (2019) Adaptive batch size image merging steganography and quantized gaussian image steganography. IEEE Transactions on Information Forensics and Security 15:867–879

    Article  Google Scholar 

  47. Sharma K, Aggarwal A, Singhania T, Gupta D, Khanna A (2019) Hiding data in images using cryptography and deep neural network. arXiv:1912.10413

  48. Sheng L, Lin Z, Shao J, Wang X (2018) Avatar-net: Multi-scale zero-shot style transfer by feature decoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8242–8250

  49. Sun Y, Zhang H, Zhang T, Wang R (2019) Deep neural networks for efficient steganographic payload location. J Real-Time Image Proc 16(3):635–647

    Article  Google Scholar 

  50. Swain G (2016) A steganographic method combining lsb substitution and pvd in a block. Procedia Computer Science 85:39–44

    Article  Google Scholar 

  51. Tang W, Li B, Tan S, Barni M, Huang J (2019) Cnn-based adversarial embedding for image steganography. IEEE Trans Inf Forensics Secur 14 (8):2074–2087

    Article  Google Scholar 

  52. Tang W, Tan S, Li B, Huang J (2017) Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Processing Letters 24(10):1547–1551

    Article  Google Scholar 

  53. Van TP, Dinh TH, Thanh TM (2019) Simultaneous convolutional neural network for highly efficient image steganography. In: 2019 19Th international symposium on communications and information technologies (ISCIT), pp 410–415. IEEE

  54. Volkhonskiy D, Nazarov I, Burnaev E (2020) Steganographic generative adversarial networks. In: Twelfth international conference on machine vision (ICMV 2019), vol. 11433, p. 114333m. International society for optics and photonics

  55. Wan W, Wang J, Zhang Y, Li J, Yu H, Sun J (2022) A comprehensive survey on robust image watermarking. Neurocomputing. Elsevier

  56. Wang X, Gupta A (2016) Generative image modeling using style and structure adversarial networks. In: European conference on computer vision, pp 318–335. Springer

  57. Wang Z, Gao N, Wang X, Xiang J, Liu G (2019) Stnet: a style transformation network for deep image steganography. In: International conference on neural information processing, pp. 3–14. Springer

  58. Weng X, Li Y, Chi L, Mu Y (2019) High-capacity convolutional video steganography with temporal residual modeling. In: Proceedings of the 2019 on international conference on multimedia retrieval, pp 87–95

  59. Wengrowski E, Dana K (2019) Light field messaging with deep photographic steganography. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1515–1524

  60. Westfeld A, Pfitzmann A (2000) Attacks on steganographic systems. information hiding. In: Third international workshop, IH, vol. 99

  61. Wu DC, Tsai WH (2003) A steganographic method for images by pixel-value differencing. Pattern Recogn Lett 24(9-10):1613–1626

    Article  MATH  Google Scholar 

  62. Xintao D, Nao L (2019) Hide the image in fc-densenets to another image. arXiv:1910.08341

  63. Xu C, Fu Y, Wen C, Pan Y, Jiang YG, Xue X (2020) Pose-guided person image synthesis in the non-iconic views. IEEE Trans Image Process 29:9060–9072

    Article  MATH  Google Scholar 

  64. Xu G (2017) Deep convolutional neural network to detect j-uniward. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp 67–73

  65. Xu H, Wang J, Kim HJ (2010) Near-optimal solution to pair-wise lsb matching via an immune programming strategy. Inf Sci 180(8):1201–1217

    Article  Google Scholar 

  66. Zhang KA, Cuesta-Infante A, Xu L, Veeramachaneni K (2019) Steganogan: High capacity image steganography with gans. arXiv:1901.03892

  67. Zhang R, Dong S, Liu J (2019) Invisible steganography via generative adversarial networks. Multimed Tools Appl 78(7):8559–8575

    Article  Google Scholar 

  68. Zheng S, Wang L, Ling B, Hu D (2017) Coverless information hiding based on robust image hashing. In: International conference on intelligent computing, pp 536–547. Springer

  69. Zhong N, Qian Z, Wang Z, Zhang X (2019) Steganography in stylized images. J Electron Imaging 28(1):033005

    Google Scholar 

  70. Zhou Z, Cao Y, Sun X (2016) Coverless information hiding based on bag-of-words model of image. J Appl Sci 34(5):527–536

    Google Scholar 

  71. Zhu J, Kaplan R, Johnson J, Fei-Fei L (2018) Hidden: Hiding data with deep networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 657–672

  72. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee, 247667, Uttarakhand, India

    Mallika Garg

  2. Electronics and Communication Engineering Department, Sant Longowal Institute of Engineering and Technology, Longowal, 148106, Punjab, India

    Jagpal Singh Ubhi

  3. Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering and Technology, Longowal, 148106, Punjab, India

    Ashwani Kumar Aggarwal

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  1. Mallika Garg
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Garg, M., Ubhi, J.S. & Aggarwal, A.K. Neural style transfer for image steganography and destylization with supervised image to image translation. Multimed Tools Appl 82, 6271–6288 (2023). https://doi.org/10.1007/s11042-022-13596-3

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