DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914849693368320 |
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| author | Ju, Yan Sun, Chengzhe Jia, Shan Hou, Shuwei Si, Zhaofeng Datta, Soumyya Kanti Ke, Lipeng Zhou, Riky Nikolich, Anita Lyu, Siwei |
| author_facet | Ju, Yan Sun, Chengzhe Jia, Shan Hou, Shuwei Si, Zhaofeng Datta, Soumyya Kanti Ke, Lipeng Zhou, Riky Nikolich, Anita Lyu, Siwei |
| contents | Deepfakes, as AI-generated media, have increasingly threatened media integrity and personal privacy with realistic yet fake digital content. In this work, we introduce an open-source and user-friendly online platform, DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting Deepfake images, videos, and audio. Built upon DeepFake-O-Meter v1.0, we have made significant upgrades and improvements in platform architecture design, including user interaction, detector integration, job balancing, and security management. The platform aims to offer everyday users a convenient service for analyzing DeepFake media using multiple state-of-the-art detection algorithms. It ensures secure and private delivery of the analysis results. Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input. We have also conducted detailed usage analysis based on the collected data to gain deeper insights into our platform's statistics. This involves analyzing two-month trends in user activity and evaluating the processing efficiency of each detector. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13146 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection Ju, Yan Sun, Chengzhe Jia, Shan Hou, Shuwei Si, Zhaofeng Datta, Soumyya Kanti Ke, Lipeng Zhou, Riky Nikolich, Anita Lyu, Siwei Cryptography and Security Computer Vision and Pattern Recognition Deepfakes, as AI-generated media, have increasingly threatened media integrity and personal privacy with realistic yet fake digital content. In this work, we introduce an open-source and user-friendly online platform, DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting Deepfake images, videos, and audio. Built upon DeepFake-O-Meter v1.0, we have made significant upgrades and improvements in platform architecture design, including user interaction, detector integration, job balancing, and security management. The platform aims to offer everyday users a convenient service for analyzing DeepFake media using multiple state-of-the-art detection algorithms. It ensures secure and private delivery of the analysis results. Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input. We have also conducted detailed usage analysis based on the collected data to gain deeper insights into our platform's statistics. This involves analyzing two-month trends in user activity and evaluating the processing efficiency of each detector. |
| title | DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection |
| topic | Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.13146 |