PGD-Trap: Proactive Deepfake Defense with Sticky Adversarial Signals and Iterative Latent Variable Refinement

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

Publisher

MDPI AG

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