Invisible steganography via generative adversarial networks
Tóm tắt
Từ khóa
Tài liệu tham khảo
Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. In: Proceedings of the 34th international conference on machine learning (ICML), pp 214–223
Baluja S (2017) Hiding images in plain sight: Deep steganography. In: Proceedings of advances in neural information processing systems 30 (NIPS), pp 2069–2079
Cachin C (1998) An information-theoretic model for steganography. In: Aucsmith D (ed) Information hiding, 2nd international workshop, volume 1525 of lecture notes in computer science, pp 306–318
Deng J, Dong W, Socher R, Li L-J, Li K, Li F-F (2009) ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 248–255
Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88:303–338
Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882
Guanshuo XU (2017) Deep convolutional neural network to detect JUNIWARD. In: Proceedings of 5th ACM workshop Inf Hiding Multimedia Secur (IH&MMSec), p 6773
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res 9:249–256
Goodfellow I, Pouget-Abadie J, Mirza M, Bing XU, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Proces 27(NIPS):2672–2680
Hayes J, Danezis G (2017) Generating steganographic images via adversarial training. In: Proceedings of advances in neural information processing systems 30 (NIPS), pp 1954–1963
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 2014(1):1–13
Huang GB, Mattar M, Lee H, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments, University of Massachusetts, Amherst. Technical Report 07-49, October
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on machine learning (ICML), vol 37, pp 448–456
Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. Proc SPIE Int Soc Opt Eng 9409:94090J–94090J-10
Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: Proceedings of international conference on learning representations (ICLR)
Rehman AU, Rahim R, Nadeem S, Hussain SU (2017) End-to-end trained cnn encoder-decoder networks for image steganography. arXiv: 1711.07201
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of international conference on medical image computing and computer-assisted intervention, pp 234–241
Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Xi C (2016) Improved techniques for training GANs. In: Proceedings of advances in neural information processing systems 29 (NIPS), pp 2234–2242
Shi H, Dong J, Wang W, Qian Y, Zhang X (2018) SSGAN: secure steganography based on generative adversarial networks. In: Zeng B, Huang Q, El Saddik A, Li H, Jiang S, Fan X (eds) Advances in multimedia information processing – PCM 2017. PCM 2017 lecture notes in computer science, vol 10735. Springer, Cham
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2818–2826
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, Borisenko B, Burnaev E (2016) Generative adversarial networks for image steganography. In ICLR 2016 Open Review
Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: The 37th asilomar conference on signals, system and computers, vol 2, pp 1398–1402
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612
Yang J, et al (2018) Spatial image steganography based on generative adversarial network. arXiv: 1804.07939
Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12:2545–2557
Zhang L, Zhang L, Mou X, Zhang D (2012) A comprehensive evaluation of full reference image quality assessment algorithms. In: Proceedings of the 19th IEEE international conference on image processing, pp 1477–1480
Zeng J et al (2018) Large-scale JPEG steganalysis using hybrid deep-learning framework. IEEE Trans Inf Forensics Secur 13:1200–1214