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Deepfake Detection Performance Evaluation and Enhancement Through Parameter Optimization

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Applied Intelligence (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2015))

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Abstract

Deepfake technology has become a subject of concern due to its potential for spreading misinformation and facilitating deceptive activities. To address these issues, various deepfake detection approaches have been developed with similar training paradigms. Then a natural question is which parameters are critical to achieving better detection performance. This study aims to evaluate and optimize the performance of existing deepfake detection systems by analyzing key parameters in the training paradigm. Specifically, we systematically analyze four crucial factors: image cropping, sampling rate, data augmentation, and transfer learning. The impact of different image scopes, such as utilizing the entire image or only the cropped face region, is investigated. We also explore how varying the sampling rate and employing data augmentation techniques can enhance the diversity of the training dataset. Additionally, transfer learning with pre-trained models is leveraged to improve detection accuracy. Through comprehensive experiments and evaluations of several popular and state-of-the-art detection methods, optimal configurations within each factor are identified, providing valuable insights to enhance the efficiency and effectiveness of deepfake detection systems. Given the widespread use and potential negative consequences of deepfake technology, reliable detection systems are crucial in combatting the harmful effects of manipulated media.

This research is supported by the National Key Research and Development Program of China (2020AAA0107702), the National Natural Science Foundation of China (62006181, 62161160337, 62132011, U21B2018, U20A20177, 62206217), the Shaanxi Province Key Industry Innovation Program (2023-ZDLGY-38, 2021ZDLGY01-02).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an , 710049, China

    Bowen Pei, Jingyi Deng, Chenhao Lin, Pengwei Hu & Chao Shen

  2. The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, China

    Pengwei Hu & Chao Shen

Authors
  1. Bowen Pei
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  2. Jingyi Deng
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  3. Chenhao Lin
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  4. Pengwei Hu
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  5. Chao Shen
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Corresponding author

Correspondence to Chenhao Lin .

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Editors and Affiliations

  1. Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Guangxi Academy of Sciences, Guangxi, China

    Changan Yuan

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Pei, B., Deng, J., Lin, C., Hu, P., Shen, C. (2024). Deepfake Detection Performance Evaluation and Enhancement Through Parameter Optimization. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2015. Springer, Singapore. https://doi.org/10.1007/978-981-97-0827-7_18

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  • DOI: https://doi.org/10.1007/978-981-97-0827-7_18

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  • Online ISBN: 978-981-97-0827-7

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