Citation: | Yiran HAN, Jianwei LIU, Xin DENG, et al., “Confidential Image Super-resolution with Privacy Protection,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2023.00.034 |
[1] |
N. F. Johnson and S. Jajodia, “Exploring steganography: Seeing the unseen,” Computer, vol. 31, no. 2, pp. 26–34, 1998. doi: 10.1109/MC.1998.4655281
|
[2] |
A. D. Ker, “A general framework for structural steganalysis of LSB replacement,” in Proceedings of the 7th International Workshop on Information Hiding, Barcelona, Spain, pp. 296–311, 2005.
|
[3] |
J. Fridrich, M. Goljan, and R. Du, “Reliable detection of LSB steganography in color and grayscale images,” in Proceedings of the 2001 Workshop on Multimedia and Security: New Challenges, Ottawa, Canada, pp. 27–30, 2001.
|
[4] |
C. T. Hsu and J. L. Wu, “Hidden digital watermarks in images,” IEEE Transactions on Image Processing, vol. 8, no. 1, pp. 58–68, 1999. doi: 10.1109/83.736686
|
[5] |
J. J. K. O. Ruanaidh, W. J. Dowling, and F. M. Boland, “Phase watermarking of digital images,” in Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland, pp. 239–242, 2008.
|
[6] |
M. Barni, F. Bartolini, and A. Piva, “Improved wavelet-based watermarking through pixel-wise masking,” IEEE Transactions on Image Processing, vol. 10, no. 5, pp. 783–791, 2001. doi: 10.1109/83.918570
|
[7] |
K. Curran and K. Bailey, “An evaluation of steganography methods,” International Journal of Digital Evidence, in press, 2003. (查阅网上资料, 未找到本条文献信息, 请确认).
K. Curran and K. Bailey, “An evaluation of steganography methods,” International Journal of Digital Evidence, in press, 2003. (查阅网上资料, 未找到本条文献信息, 请确认).
|
[8] |
A. Westfeld and A. Pfitzmann, “Attacks on steganographic systems: Breaking the Steganographic utilities EzStego, Jsteg, Steganos, and S-Tools-and some lessons learned,” in Proceedings of the Third International Workshop on Information Hiding, Dresden, Germany, pp. 61–76, 2000.
|
[9] |
N. Provos and P. Honeyman, “Hide and seek: An introduction to steganography,” IEEE Security & Privacy, vol. 1, no. 3, pp. 32–44, 2003. doi: 10.1109/MSECP.2003.1203220
|
[10] |
S. Baluja, “Hiding images in plain sight: Deep steganography,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp. 2066–2076, 2017.
|
[11] |
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. doi: 10.1126/science.1127647
|
[12] |
P. Wu, Y. Yang, and X. Q. Li, “StegNet: Mega image steganography capacity with deep convolutional network,” Future Internet, vol. 10, no. 6, article no. 54, 2018. doi: 10.3390/fi10060054
|
[13] |
J. R. Zhu, R. Kaplan, J. Johnson, et al., “HiDDeN: Hiding data with deep networks,” in Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany, pp. 682–697, 2018.
|
[14] |
X. Y. Weng, Y. Z. Li, L. Chi, et al., “High-capacity convolutional video steganography with temporal residual modeling,” in Proceedings of the 2019 on International Conference on Multimedia Retrieval, Ottawa, Canada, pp. 87–95, 2019.
|
[15] |
D. X. Dai, Y. J. Wang, Y. H. Chen, et al., “Is image super-resolution helpful for other vision tasks?” in Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, pp. 1–9, 2016.
|
[16] |
S. Thurnhofer and S. K. Mitra, “Edge-enhanced image zooming,” Optical Engineering, vol. 35, no. 7, pp. 1862–1870, 1996. doi: 10.1117/1.600619
|
[17] |
F. Fekri, R. M. Mersereau, and R. W. Schafer, “A generalized interpolative VQ method for jointly optimal quantization and interpolation of images,” in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, WA, USA, pp. 2657–2660, 1998.
|
[18] |
W. T. Freeman and E. C. Pasztor, “Learning low-level vision,” in Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1182–1189, 1999.
|
[19] |
H. Chang, D. Y. Yeung, and Y. M. Xiong, “Super-resolution through neighbor embedding,” in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 2004.
|
[20] |
J. C. Yang, J. Wright, T. Huang, et al., “Image super-resolution as sparse representation of raw image patches,” in Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp. 1–8, 2008.
|
[21] |
R. Timofte, V. De, and L. Van Gool, “Anchored neighborhood regression for fast example-based super-resolution,” in Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia, pp. 1920–1927, 2013.
|
[22] |
R. Timofte, V. De Smet, and L. Van Gool, “A+: Adjusted anchored neighborhood regression for fast super-resolution,” in Proceedings of the 12th Asian Conference on Computer Vision, Singapore, Singapore, pp. 111–126, 2015.
|
[23] |
C. Dong, C. C. Loy, K. M. He, et al., “Learning a deep convolutional network for image super-resolution,” in Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, pp. 184–199, 2014.
|
[24] |
J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 1646–1654, 2016.
|
[25] |
C. Dong, C. C. Loy, and X. O. Tang, “Accelerating the super-resolution convolutional neural network,” in Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 391–407, 2016.
|
[26] |
K. M. He, X. Y. Zhang, S. Q. Ren, et al., “Deep residual learning for image recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 770–778, 2016.
|
[27] |
B. Lim, S. Son, H. Kim, et al., “Enhanced deep residual networks for single image super-resolution,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, pp. 1132–1140, 2017.
|
[28] |
C. Ledig, L. Theis, F. Huszar, et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 105–114, 2017.
|
[29] |
Y. Tai, J. Yang, X. M. Liu, et al., “MemNet: A persistent memory network for image restoration,” in Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy, pp. 4549–4557, 2017.
|
[30] |
Y. L. Zhang, K. P. Li, K. Li, et al., “Image super-resolution using very deep residual channel attention networks,” in Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany, pp. 294–310, 2018.
|
[31] |
J. S. Choi and M. Kim, “A deep convolutional neural network with selection units for super-resolution,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, pp. 1150–1156, 2017.
|
[32] |
G. Huang, Z. Liu, L. Van Der Maaten, et al., “Densely connected convolutional networks,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 2261–2269, 2016.
|
[33] |
Y. L. Zhang, Y. P. Tian, Y. Kong, et al., “Residual dense network for image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 7, pp. 2480–2495, 2021. doi: 10.1109/TPAMI.2020.2968521
|
[34] |
M. Haris, G. Shakhnarovich, and N. Ukita, “Deep back-projection networks for super-resolution,” in Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 1664–1673, 2018.
|
[35] |
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., “Generative adversarial nets,” in Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 2672–2680, 2014.
|
[36] |
T. Y. Lin, M. Maire, S. Belongie, et al., “Microsoft COCO: Common objects in context,” in Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, pp. 740–755, 2014.
|
[37] |
E. Agustsson and R. Timofte, “NTIRE 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, pp. 1122–1131, 2017.
|
[38] |
J. Ye, J. Q. Ni, and Y. Yi, “Deep learning hierarchical representations for image steganalysis,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 11, pp. 2545–2557, 2017. doi: 10.1109/TIFS.2017.2710946
|
[39] |
Y. M. Hei, L. H. Wang, J. W. Sheng, et al., “Label graph augmented soft cascade decoding model for overlapping event extraction,” International Journal of Machine Learning and Cybernetics, vol. 15, no. 1, pp. 79–95, 2024. doi: 10.1007/s13042-022-01760-y
|
[40] |
M. Z. Konyar and S. Solak, “Efficient data hiding method for videos based on adaptive inverted lsb332 and secure frame selection with enhanced vigenere cipher,” Journal of Information Security and Applications, vol. 63, article no. 103037, 2021. doi: 10.1016/j.jisa.2021.103037
|
[41] |
S. Kaçar, M. Z. Konyar, and Ünal Çavuşoğlu, “4D chaotic system-based secure data hiding method to improve robustness and embedding capacity of videos,” Journal of Information Security and Applications, vol. 71, article no. 103369, 2022. doi: 10.1016/j.jisa.2022.103369
|