DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014

2014

Earlier work this paper cites.

J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International conference on machine learning . PMLR, 2015, pp. 2256–2265

2015

Earlier work this paper cites.

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232

2017

Earlier work this paper cites.

A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” arXiv preprint arXiv:1809.11096 , 2018

Original

2018

Earlier work this paper cites.

D. Cozzolino and L. Verdoliva, “Noiseprint: A cnn-based camera model fingerprint,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 144–159, 2019

2019

Earlier work this paper cites.

Y. Wu, W. AbdAlmageed, and P. Natarajan, “Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9543–9552

2019

Earlier work this paper cites.

K. Sun, B. Xiao, D. Liu, and J. Wang, “Deep high-resolution representation learning for human pose estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 5693–5703

2019

Earlier work this paper cites.

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020

2020

Earlier work this paper cites.

A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741 , 2021

Original

2021

Earlier work this paper cites.

H. Ling, K. Kreis, D. Li, S. W. Kim, A. Torralba, and S. Fidler, “Editgan: High-precision semantic image editing,” Advances in Neural Information Processing Systems , vol. 34, pp. 16 331–16 345, 2021

2021

Earlier work this paper cites.

X. Liu, C. Gong, L. Wu, S. Zhang, H. Su, and Q. Liu, “Fusedream: Training-free text-to-image generation with improved clip+ gan space optimization,” arXiv preprint arXiv:2112.01573 , 2021

Original

2021

Earlier work this paper cites.

Y. Huang, S. Bian, H. Li, C. Wang, and K. Li, “Ds-unet: A dual streams unet for refined image forgery localization,” Information Sciences , vol. 610, pp. 73–89, 2022

2022

Earlier work this paper cites.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

2022

Cited alongside, same era.

C. Dong, X. Chen, R. Hu, J. Cao, and X. Li, “Mvss-net: Multi-view multi-scale supervised networks for image manipulation detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 3, pp. 3539–3553, 2022

2022

Cited alongside, same era.

X. Liu, Y. Liu, J. Chen, and X. Liu, “Pscc-net: Progressive spatio-channel correlation network for image manipulation detection and localization,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 11, pp. 7505–7517, 2022

2022

Cited alongside, same era.

Q. Liu, H. Li, and Z. Liu, “Image forgery localization based on fully convolutional network with noise feature,” Multimedia Tools and Applications , vol. 81, no. 13, pp. 17 919–17 935, 2022

J. Zhu, C. Yang, Y. Shen, Z. Shi, B. Dai, D. Zhao, and Q. Chen, “Linkgan: Linking gan latents to pixels for controllable image synthesis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7656–7666

2023

Later among the works it cites.

A. Sauer, T. Karras, S. Laine, A. Geiger, and T. Aila, “Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis,” arXiv preprint arXiv:2301.09515 , 2023

Original

2023

Later among the works it cites.

M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park, “Scaling up gans for text-to-image synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 10 124–10 134

2023

Later among the works it cites.

X. Pan, A. Tewari, T. Leimkühler, L. Liu, A. Meka, and C. Theobalt, “Drag your gan: Interactive point-based manipulation on the generative image manifold,” in ACM SIGGRAPH 2023 Conference Proceedings , 2023, pp. 1–11

2023