Capturing the Persistence of Facial Expression Features for Deepfake Video Detection

Zhao, Yijia; Ge, Wanfeng; Li, Wenxin; Wang, Run; Zhao, Lei; Ming, Jiang

doi:10.1007/978-3-030-41579-2_37

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…Except for the robustness of algorithms, generalization ability is also essential for forgery detection tasks. Zhao et al [48] used optical flow to capture the obvious differences of facial expressions between adjacent frames. However, these studies did not show strong generalization or robustness.…”

Section: Improvementmentioning

confidence: 99%

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A Survey on Deepfake Video Detection

et al. 2021

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Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism. So far, there have been a large amount of deepfake videos circulating on the Internet, most of which target at celebrities or politicians. These videos are often used to damage the reputation of celebrities and guide public opinion, greatly threatening social stability. Although the deepfake algorithm itself has no attributes of good or evil, this technology has been widely used for negative purposes. To prevent it from threatening human society, a series of research have been launched, including developing detection methods and building large-scale benchmarks. This review aims to demonstrate the current research status of deepfake video detection, especially, generation process, several detection methods and existing benchmarks. It has been revealed that current detection methods are still insufficient to be applied in real scenes, and further research should pay more attention to the generalization and robustness.This is an open access article under the terms of the Creative Commons Attribution-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited and no modifications or adaptations are made.

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Section: Improvementmentioning

confidence: 99%

“…Zhao et al. [48] used optical flow to capture the obvious differences of facial expressions between adjacent frames. However, these studies did not show strong generalization or robustness.…”

Section: Deepfake Video Detectionmentioning

confidence: 99%

A Survey on Deepfake Video Detection

et al. 2021

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“…In the current research of DeepFake forensics, the Deep-Fake detection (Wang et al 2020b;Zhao et al 2020) tells us whether the sample is real or fake, while the DeepFake attribution 1 aims at investigating which forgery model is employed for creating such DeepFakes, further providing explainable results for DeepFake detection. In this paper, we explore an interesting question, whether the existing Deep-Fake attribution techniques are robust enough to serve for DeepFake forensics and the potentials in deploying in real scenarios.…”

Section: Introductionmentioning

confidence: 99%

TraceEvader: Making DeepFakes More Untraceable via Evading the Forgery Model Attribution

Wu,

Ma,

Wang

et al. 2024

AAAI

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In recent few years, DeepFakes are posing serve threats and concerns to both individuals and celebrities, as realistic DeepFakes facilitate the spread of disinformation. Model attribution techniques aim at attributing the adopted forgery models of DeepFakes for provenance purposes and providing explainable results to DeepFake forensics. However, the existing model attribution techniques rely on the trace left in the DeepFake creation, which can become futile if such traces were disrupted. Motivated by our observation that certain traces served for model attribution appeared in both the high-frequency and low-frequency domains and play a divergent role in model attribution. In this work, for the first time, we propose a novel training-free evasion attack, TraceEvader, in the most practical non-box setting. Specifically, TraceEvader injects a universal imitated traces learned from wild DeepFakes into the high-frequency component and introduces adversarial blur into the domain of the low-frequency component, where the added distortion confuses the extraction of certain traces for model attribution. The comprehensive evaluation on 4 state-of-the-art (SOTA) model attribution techniques and fake images generated by 8 generative models including generative adversarial networks (GANs) and diffusion models (DMs) demonstrates the effectiveness of our method. Overall, our TraceEvader achieves the highest average attack success rate of 79% and is robust against image transformations and dedicated denoising techniques as well where the average attack success rate is still around 75%. Our TraceEvader confirms the limitations of current model attribution techniques and calls the attention of DeepFake researchers and practitioners for more robust-purpose model attribution techniques.

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Deepfakes: evolution and trends

et al. 2023

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This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

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Capturing the Persistence of Facial Expression Features for Deepfake Video Detection

Cited by 13 publications

References 9 publications

A Survey on Deepfake Video Detection

A Survey on Deepfake Video Detection

TraceEvader: Making DeepFakes More Untraceable via Evading the Forgery Model Attribution

Deepfakes: evolution and trends

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