Towards Understanding of Deepfake Videos in the Wild
- URL: http://arxiv.org/abs/2309.01919v2
- Date: Wed, 6 Sep 2023 08:57:13 GMT
- Title: Towards Understanding of Deepfake Videos in the Wild
- Authors: Beomsang Cho, Binh M. Le, Jiwon Kim, Simon Woo, Shahroz Tariq,
Alsharif Abuadbba, Kristen Moore
- Abstract summary: We present the largest and most diverse deepfake dataset (RWDF-23) collected from the wild to date.
By expanding the dataset's scope beyond the previous research, we capture a broader range of real-world deepfake content.
Also, we conduct a comprehensive analysis encompassing various aspects of deepfakes, including creators, manipulation strategies, purposes, and real-world content production methods.
- Score: 17.76886643741705
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deepfakes have become a growing concern in recent years, prompting
researchers to develop benchmark datasets and detection algorithms to tackle
the issue. However, existing datasets suffer from significant drawbacks that
hamper their effectiveness. Notably, these datasets fail to encompass the
latest deepfake videos produced by state-of-the-art methods that are being
shared across various platforms. This limitation impedes the ability to keep
pace with the rapid evolution of generative AI techniques employed in
real-world deepfake production. Our contributions in this IRB-approved study
are to bridge this knowledge gap from current real-world deepfakes by providing
in-depth analysis. We first present the largest and most diverse and recent
deepfake dataset (RWDF-23) collected from the wild to date, consisting of 2,000
deepfake videos collected from 4 platforms targeting 4 different languages span
created from 21 countries: Reddit, YouTube, TikTok, and Bilibili. By expanding
the dataset's scope beyond the previous research, we capture a broader range of
real-world deepfake content, reflecting the ever-evolving landscape of online
platforms. Also, we conduct a comprehensive analysis encompassing various
aspects of deepfakes, including creators, manipulation strategies, purposes,
and real-world content production methods. This allows us to gain valuable
insights into the nuances and characteristics of deepfakes in different
contexts. Lastly, in addition to the video content, we also collect viewer
comments and interactions, enabling us to explore the engagements of internet
users with deepfake content. By considering this rich contextual information,
we aim to provide a holistic understanding of the {evolving} deepfake
phenomenon and its impact on online platforms.
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