PGD-Trap: Proactive Deepfake Defense with Sticky Adversarial Signals and Iterative Latent Variable Refinement
-
Published:2024-08-23
Issue:17
Volume:13
Page:3353
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Zhuang Zhong1, Tomioka Yoichi2ORCID, Shin Jungpil2ORCID, Okuyama Yuichi2
Affiliation:
1. Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan 2. School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
Abstract
With the development of artificial intelligence (AI), deepfakes, in which the face of one person is changed to another expression of the same person or a different person, have advanced. There is a need for countermeasures against crimes that exploit deepfakes. Methods to interfere with deepfake generation by adding an invisible weak adversarial signal to an image have been proposed. However, there is a problem: the weak signal can be easily removed by processing the image. In this paper, we propose trap signals that appear in response to a process that weakens adversarial signals. We also propose a new type of adversarial signal injection that allow us to reconstruct and change the original image as far as people do not feel strange by Denoising Diffusion Probabilistic Model (DDPM)-based Iterative Latent Variable Refinement. In our experiments with Star Generative Adversarial Network (StarGAN) trained with the CelebFaces Attributes (CelebA) Dataset, we demonstrate that the proposed approach achieves more robust proactive deepfake defense.
Funder
Sumitomo Electric Industries Group CSR Foundation
Reference27 articles.
1. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8–13). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada. 2. Karras, T., Laine, S., and Aila, T. (2019, January 15–20). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. 3. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13–19). Analyzing and improving the image quality of stylegan. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA. 4. Liu, M., Ding, Y., Xia, M., Liu, X., Ding, E., Zuo, W., and Wen, S. (2019, January 15–20). Stgan: A unified selective transfer network for arbitrary image attribute editing. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. 5. Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., and Choo, J. (2018, January 18–23). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.
|
|