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Joint source-channel coding (JSCC) has achieved great success due to the introduction of deep learning (DL).
R. Davis, “The data encryption standard in perspective,” IEEE Communications Society Magazine , vol. 16, no. 6, pp. 5–9, 1978
1978
Earlier work this paper cites.
R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Communications of the ACM , vol. 21, no. 2, pp. 120–126, 1978
1978
Earlier work this paper cites.
T. M. Cover, Elements of Information Theory . John Wiley & Sons, 1999
1999
Earlier work this paper cites.
R. W. Yeung and Z. Zhang, “Distributed source coding for satellite communications,” IEEE Transactions on Information Theory , vol. 45, no. 4, pp. 1111–1120, 1999
1999
Earlier work this paper cites.
M. Skoglund, N. Phamdo, and F. Alajaji, “Hybrid digital–analog source–channel coding for bandwidth compression/expansion,” IEEE Transactions on Information Theory , vol. 52, no. 8, pp. 3757–3763, 2006
2006
Earlier work this paper cites.
Y. Mao and M. Wu, “A joint signal processing and cryptographic approach to multimedia encryption,” IEEE Transactions on Image Processing , vol. 15, no. 7, pp. 2061–2075, 2006
2006
Earlier work this paper cites.
G.-h. Chen, C.-l. Yang, and S.-l. Xie, “Gradient-based structural similarity for image quality assessment,” in 2006 International Conference on Image Processing , 2006, pp. 2929–2932
2006
Earlier work this paper cites.
Y. Zhong, F. Alajaji, and L. L. Campbell, “Joint source–channel coding error exponent for discrete communication systems with markovian memory,” IEEE Transactions on Information Theory , vol. 53, no. 12, pp. 4457–4472, 2007
2007
Earlier work this paper cites.
S. Heron, “Advanced encryption standard (AES),” Network Security , vol. 2009, no. 12, pp. 8–12, 2009
2009
Earlier work this paper cites.
L. Wu, J. Zhang, W. Deng, and D. He, “Arnold transformation algorithm and anti-arnold transformation algorithm,” in 2009 First International Conference on Information Science and Engineering , 2009, pp. 1164–1167
2009
Earlier work this paper cites.
B. Acharya, S. K. Panigrahy, S. K. Patra, and G. Panda, “Image encryption using advanced hill cipher algorithm,” International Journal of Recent Trends in Engineering , vol. 1, no. 1, pp. 663–667, 2009
2009
Earlier work this paper cites.
L. Tong, F. Dai, Y. Zhang, and J. Li, “Visual security evaluation for video encryption,” in Proceedings of the 18th ACM international conference on Multimedia , 2010, pp. 835–838
2010
Earlier work this paper cites.
A. Kanso and M. Ghebleh, “A novel image encryption algorithm based on a 3D chaotic map,” Communications in Nonlinear Science and Numerical Simulation , vol. 17, no. 7, pp. 2943–2959, 2012
2012
Earlier work this paper cites.
X. Kang, A. Peng, X. Xu, and X. Cao, “Performing scalable lossy compression on pixel encrypted images,” EURASIP Journal on Image and Video Processing , vol. 2013, no. 1, pp. 1–6, 2013
2013
Earlier work this paper cites.
J. Zhou, X. Liu, O. C. Au, and Y. Y. Tang, “Designing an efficient image encryption-then-compression system via prediction error clustering and random permutation,” IEEE Transactions on Information Forensics and Security , vol. 9, no. 1, pp. 39–50, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Cited alongside, same era.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision . Springer, 2016, pp. 694–711
2016
Cited alongside, same era.
2016
Cited alongside, same era.
G. Yue, C. Hou, K. Gu, T. Zhou, and H. Liu, “No-reference quality evaluator of transparently encrypted images,” IEEE Transactions on Multimedia , vol. 21, no. 9, pp. 2184–2194, 2019
2019
Later among the works it cites.
W. Sirichotedumrong, T. Maekawa, Y. Kinoshita, and H. Kiya, “Privacy-preserving deep neural networks with pixel-based image encryption considering data augmentation in the encrypted domain,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 674–678
2019
Later among the works it cites.
——, “Deepjscc-f: Deep joint source-channel coding of images with feedback,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 1, pp. 178–193, 2020
2020
Later among the works it cites.
T. Xiang, Y. Yang, H. Liu, and S. Guo, “Visual security evaluation of perceptually encrypted images based on image importance,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 11, pp. 4129–4142, 2020
2020
Later among the works it cites.
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
V. Kostina, Y. Polyanskiy, and S. Verd, “Joint source-channel coding with feedback,” IEEE Transactions on Information Theory , vol. 63, no. 6, pp. 3502–3515, 2017
2017
Cited alongside, same era.
O. F. Mohammad, M. S. M. Rahim, S. R. M. Zeebaree, and F. Ahmed, “A survey and analysis of the image encryption methods,” International Journal of Applied Engineering Research , vol. 12, no. 23, pp. 13 265–13 280, 2017
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
2017
Cited alongside, same era.
C. Li, X. Guang, C. W. Tan, and R. W. Yeung, “Fundamental limits on a class of secure asymmetric multilevel diversity coding systems,” IEEE Journal on Selected Areas in Communications , vol. 36, no. 4, pp. 737–747, 2018
2018
Cited alongside, same era.
N. Farsad, M. Rao, and A. Goldsmith, “Deep learning for joint source-channel coding of text,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 2326–2330
2018
Cited alongside, same era.
M. Tanaka, “Learnable image encryption,” in 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) . IEEE, 2018, pp. 1–2
2018
Cited alongside, same era.
E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint source-channel coding for wireless image transmission,” IEEE Transactions on Cognitive Communications and Networking , vol. 5, no. 3, pp. 567–579, 2019
2019
Cited alongside, same era.
Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 8, pp. 2434–2444, 2021
2021
Closest in time.
D. B. Kurka and D. Gündüz, “Bandwidth-agile image transmission with deep joint source-channel coding,” IEEE Transactions on Wireless Communications , vol. 20, no. 12, pp. 8081–8095, 2021
2021
Closest in time.
H. Ito, Y. Kinoshita, M. Aprilpyone, and H. Kiya, “Image to perturbation: An image transformation network for generating visually protected images for privacy-preserving deep neural networks,” IEEE Access , vol. 9, pp. 64 629–64 638, 2021
2021
Closest in time.
W. Sirichotedumrong and H. Kiya, “A GAN-based image transformation scheme for privacy-preserving deep neural networks,” in 2020 28th European Signal Processing Conference (EUSIPCO) . IEEE, 2021, pp. 745–749
2021
Closest in time.
W. Wen, K. Wei, Y. Fang, and Y. Zhang, “Visual quality assessment for perceptually encrypted light field images,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 7, pp. 2522–2534, 2021
2021
Closest in time.
Y. Yang, T. Xiang, H. Liu, and X. Liao, “Convolutional neural network for visual security evaluation,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 8, pp. 3293–3307, 2021
2021
Closest in time.
J. Xu, B. Ai, N. Wang, and W. Chen, “Deep joint source-channel coding for csi feedback: An end-to-end approach,” IEEE Journal on Selected Areas in Communications , 2022, accepted
2022
Closest in time.
J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. Rodrigues, “Wireless image transmission using deep source channel coding with attention modules,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 4, pp. 2315–2328, 2022
2022
Closest in time.
M. Yang, C. Bian, and H.-S. Kim, “OFDM-guided deep joint source channel coding for wireless multipath fading channels,” IEEE Transactions on Cognitive Communications and Networking , 2022
2022
Closest in time.
2022
Closest in time.
S. Wang, K. Yang, J. Dai, and K. Niu, “Distributed image transmission using deep joint source-channel coding,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 5208–5212
2022
Closest in time.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2023
Closest in time.