References
- Alec, Radford, et al, "Improving language understanding by generative pre-training.", 2018.
- Goodfellow, Ian, et al, "Generative adversarial networks.", Communications of the ACM 63.11, 139-144, 2020. https://doi.org/10.1145/3422622
- Ajay Jain, Ho, Jonathan, and Pieter Abbeel, "Denoising diffusion probabilistic models.", Advances in Neural Information Processing Systems 33, pp.6840-6851, 2020.
- Robin, Rombach, et al, "High-resolution image synthesis with latent diffusion models.", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.10684-10695, 2022.
- Alex, Nichol, et al, "Glide: Towards photorealistic image generation and editing with text-guided diffusion models.", arXiv preprint arXiv:2112.10741, 2021.
- Chitwan, Saharia, et al, "Photorealistic text-to-image diffusion models with deep language understanding.", arXiv preprint arXiv:2205.11487, 2022.
- Diederik P., Max Welling, Kingma, "Auto-encoding variational bayes.", arXiv preprint arXiv:1312.6114, 2013.
- Ashish, Vaswani, et al, "Attention is all you need.", Advances in neural information processing systems 30, 2017.
- 최선, "의사 시험까지 합격한 chatGPT "과한 기대는 금물"", 2023년 2월 23일자.
- 김효영, "ChatGPT 과제 0점처리...철학없는 인공지능은 비교육적", 2023년 2월 20일자.
- 공인호, "챗GPT로 작가 등단...저작권 논란 가열", 포춘코리아, 2023년 2월 23일자.
- 이상덕, ""나는 살아있다"...공포감 들게 한 소름 돋는 답변의 정체는", 매일경제, 2023년 2월 16일자.
- Jun-Yan, Zhu, et al, "Unpaired image-to-image translation using cycle-consistent adversarial networks.", Proceedings of the IEEE international conference on computer vision, pp. 2223-2232, 2017.
- Choi, Yunjey, et al, "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation.", Proceedings of the IEEE conference on computer vision and pattern recognition, 2018.
- Karras, Samuli Laine, Tero, and Timo Aila, "A style-based generator architecture for generative adversarial networks.", Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4401-4410, 2019.
- Jiahui, Yu, et al, "Scaling autoregressive models for content-rich text-to-image generation.", arXiv preprint arXiv:2206.10789, 2022.
- Aditya, Ramesh, et al, "Zero-shot text-to-image generation.", International Conference on Machine Learning. PMLR, 2021.
- Aditya, Ramesh, et al, "Hierarchical textconditional image generation with clip latents.", arXiv preprint arXiv:2204.06125, 2022.
- 김송이, "미술전 1등 작품, ○○가 그렸다고?...논란된 작가, 누구길래", 뉴스1, 2022년 9월 5일자.
- 임병선, "김정기 작가 세상 뜨자마자 AI 학습한 그림 올리고 "오마주"", 서울신문, 2022년 10월 10일자.
- Bhiksha Raj, Gao, Rita Singh, and Yang,. "Voice impersonation using generative adversarial networks.", 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018.
- Donahue, Chris, Julian McAuley, and Miller Puckette, "Adversarial audio synthesis.", arXiv preprint arXiv:1802.04208, 2018.
- Borsos, Zalan, et al, "Audiolm: a language modeling approach to audio generation.", arXiv preprint arXiv:2209.03143, 2022.
- Huang, Qingqing, et al, "Noise2Music: Text-conditioned Music Generation with Diffusion Models.", arXiv preprint arXiv:2302.03917, 2023.
- 황희진, "아마존 AI '알렉사'의 살인미수? 10살 소녀에 "전기 콘센트에 동전 갖다 대"", 매일신문, 2021년 12월 29일자.
- 정경훈, 김창현, "[단독]"목소리 소름주의"...400억 가로챈 '딥보이스 범죄', 檢도 나섰다", 머니투데이, 2023년 2월 11일자.
- Ahmed, Salem, et al, "Baaan: Backdoor attacks against autoencoder and gan-based machine learning models.", arXiv preprint arXiv:2010.03007, 2020.
- Ambrish, Killian Levacher, Mathieu Sinn, and Rawat, "The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models.", Computer Security- ESORICS 2022: 27th European Symposium on Research in Computer Security, Copenhagen, Denmark, September 26-30, 2022, Proceedings, Part III. Cham: Springer Nature Switzerland, pp. 776-783, 2022.
- Dominik Hintersdorf, Kristian Kersting, Lukas, and Struppek, "Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models.", arXiv preprint arXiv:2211.02408, 2022.
- Chou, Pin-Yu Chen, Sheng-Yen, and Tsung-Yi Ho, "How to Backdoor Diffusion Models?.", arXiv preprint arXiv:2212.05400, 2022.
- Myle, Ott, et al, "fairseq: A fast, extensible toolkit for sequence modeling.", arXiv preprint arXiv:1904.01038, 2019.
- Sun, Xiaofei, et al, "Defending against backdoor attacks in natural language generation.", arXiv preprint arXiv:2106.01810, 2021.
- Hailong, Hu, and Jun Pang, "Model extraction and defenses on generative adversarial networks.", arXiv preprint arXiv: 2101.02069, 2021.
- Miyato, Takeru, et al, "Spectral normalization for generative adversarial networks.", arXiv preprint arXiv:1802.05957, 2018.
- Karras, Tero, et al, "Progressive growing of gans for improved quality, stability, and variation.", arXiv preprint arXiv:1710.10196, 2017.
- Hayes, Jamie, et al, "Logan: Membership inference attacks against generative models.", arXiv preprint arXiv:1705.07663, 2017.
- Luke Metz, Radford, Alec, and Soumith Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks.", arXiv preprint arXiv:1511.06434, 2015.
- Berthelot, David, Luke Metz, and Thomas Schumm, "Began: Boundary equilibrium generative adversarial networks.", arXiv preprint arXiv:1703.10717, 2017.
- Benjamin, Daniel Bernau, Hilprecht, and Martin Harterich, "Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models.", Proc. Priv. Enhancing Technol, 232-249, 2019
- Chen, Dingfan, et al, "Gan-leaks: A taxonomy of membership inference attacks against generative models.", Proceedings of the 2020 ACM SIGSAC conference on computer and communications security, pp. 343-362, 2020.
- Wu, Yixin, et al, "Membership Inference Attacks Against Text-to-image Generation Models.", arXiv preprint arXiv:2210.00968, 2022.
- Duan, Jinhao, et al, "Are Diffusion Models Vulnerable to Membership Inference Attacks?.", arXiv preprint arXiv:2302.01316, 2023.
- Carlini, Nicholas, et al, "Extracting training data from diffusion models.", arXiv preprint arXiv:2301.13188, 2023.