Deepfake Video Detection on Social Networks Using Multi-domain Aware Driven by Common Mechanism Analysis Between Artifacts

WANG Yan; SUN Qindong; RONG Dongzhu; WANG Xiaoxiong

doi:10.11999/JEIT240025

Volume 46 Issue 9

Sep. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3713-3721

WANG Yan, SUN Qindong, RONG Dongzhu, WANG Xiaoxiong. Deepfake Video Detection on Social Networks Using Multi-domain Aware Driven by Common Mechanism Analysis Between Artifacts[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3713-3721. doi: 10.11999/JEIT240025

Citation:

WANG Yan, SUN Qindong, RONG Dongzhu, WANG Xiaoxiong. Deepfake Video Detection on Social Networks Using Multi-domain Aware Driven by Common Mechanism Analysis Between Artifacts[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3713-3721. doi: 10.11999/JEIT240025

Citation:

WANG Yan, SUN Qindong, RONG Dongzhu, WANG Xiaoxiong. Deepfake Video Detection on Social Networks Using Multi-domain Aware Driven by Common Mechanism Analysis Between Artifacts[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3713-3721. doi: 10.11999/JEIT240025

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Deepfake Video Detection on Social Networks Using Multi-domain Aware Driven by Common Mechanism Analysis Between Artifacts

doi: 10.11999/JEIT240025

WANG Yan¹,
SUN Qindong^{1, 2
,
,},
RONG Dongzhu¹,
WANG Xiaoxiong^{1, 3}

1.
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2.
School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3.
Sichuan Digital Economy Industry Development Research Institute, Chengdu 610036, China

Funds: The National Natural Science Foundation of China (62272378), Shaanxi Province Key Research and Development Program (2022ZDLSF07-07), The Natural Science Foundation of Sichuan Province (2023NSFSC0502)

Received Date: 2024-01-18
Rev Recd Date: 2024-06-13

Available Online: 2024-06-18

Publish Date: 2024-09-26

Abstract

Abstract

The misuse of deepfake technology on social networks has raised serious concerns about the authenticity and reliability of visual content. The degradation phenomenon of deepfake videos on social networks has not been adequately considered in existing detection algorithms, resulting in deepfake detection performance being limited by challenging issues such as compression artifacts interference and lack of context-related information. Compression encoding and up-sampling operations in deepfake generation algorithms can leave artifacts on videos, which can result in fine-grained differences between real videos and deepfake videos. The common mechanisms between compression artifacts and deepfake artifacts are analyzed to reveal the structural similarities between them, which provides a reliable theoretical basis for enhancing the robustness of deepfake detection models against compression. Firstly, to address the interference of compression noise on deepfake features, the frequency-domain adaptive notch filter is designed based on the structural similarity of compression artifacts and deepfake artifacts to eliminate the interference of compression artifacts on specific frequency bands. Secondly, the denoising branch based on residual learning is designed to reduce the sensitivity of the deepfake detection model to unknown noise. Additionally, the attention-based feature fusion method is adopted to enhance the discriminative features of deepfakes. Metric learning strategies are adopted to optimize network models, achieving deepfake detection with resistance to compression. Theoretical analysis and experimental results indicate that the detection performance of compressed deepfake videos is significantly enhanced by using the algorithm proposed in this paper. It can be used as a plug-and-play model combined with existing detection methods to enhance their robustness against compression.
- Deepfake detection,
- Social network,
- Compressed deepfake,
- Common mechanism between artifacts

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References(23)

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