Acknowledgement
이 논문은 2023년도 정부(문화체육관광부)의 재원으로 한국콘텐츠진흥원의 지원을 받아 수행된 연구임(No.2021-ec-9500S2, 교육 콘텐츠에 대한 인공지능 기반 저작권 침해 의심요소 검출 및 대체 재료 콘텐츠 추천 기술 개발).
References
- L. Du, A. T. Ho, and R. Cong, "Perceptual hashing for image authentication: A survey," Signal Processing: Image Communication, Vol.81, pp.115713, 2020.
- J. Mao, D. Zhong, Y. Hu, W. Sheng, G. Xiao, and Z. Qu, "An image authentication technology based on depth residual network," Systems Science & Control Engineering, Vol.6, No.1, pp.57-70, 2018.
- D. Kim, S. Heo, J. Kang, H. Kang, and S. Lee, "A photo identification framework to prevent copyright infringement with manipulations," Applied Sciences, Vol.11, No.19, pp. 9194, 2021.
- S. Zhu, C. Zhu, and W. Wang, "A new image encryption algorithm based on chaos and secure hash sha-256," Entropy, Vol.20, No.9, pp.716, 2018.
- L. W. Kang, C. S. Lu, and C. Y. Hsu, "Compressive sensing-based image hashing," in 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE, pp.1285-1288, 2009.
- K. P. Murphy, "Probabilistic Machine Learning: An introduction," MIT Press, 2022.
- G. E Hinton, A. Krizhevsky, and S. D. Wang, "Transforming auto-encoders," in Artificial Neural Networks and Machine Learning-ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I 21. Springer, pp.44-51, 2011.
- D. P. Kingma and M. Welling, "Auto-encoding variational bayes," in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014.
- X. Chen, Y. SUN, M. Zhang, and D. Peng, "Evolving deep convolutional variational autoencoders for image classification," IEEE Transactions on Evolutionary Computation, Vol.25, No.5, pp.815-829, 2020. https://doi.org/10.1109/TEVC.2020.3047220
- A. Dosovitskiy et al., "An image is worth 16×16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.
- L. Yang, R. Y. Zhang, L. Li, and X. Xie, "Simam: A simple, parameter-free attention module for convolutional neural networks," in Proceedings of the 38th International Conference on Machine Learning, Marina Meila and Tong Zhang, Eds. 18-24 Jul 2021, Vol. 139 of Proceedings of Machine Learning Research, pp.11863-11874, PMLR.
- H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan, "Deep hashing network for unsupervised domain adaptation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5018-5027, 2017.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, Vol.25, 2012.
- K. Simonyan and A. Zisserman. "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
- V. Gattupalli, Y. Zhuo, and B. Li, "Weakly supervised deep image hashing through tag embeddings," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.10375-10384, 2019.
- H. Liu, R. Wang, S. Shan, and X. Chen, "Deep supervised hashing for fast image retrieval," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
- Z. Cao, M. Long, J. Wang, and P. S. Yu, "Hashnet: Deep learning to hash by continuation," Proceedings of the IEEE International Conference on Computer Vision, 2017.
- S. R. Dubey, S. K. Singh, and W. T. Chu. "Vision transformer hashing for image retrieval," 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022.