Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM Article Swipe
Rushit Dave
,
Mounika vanamala
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.22123094.v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.22123094.v1
Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one’s face with a computer-generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms; support vector machine and convolutional neural network, along with a publicly available dataset named the 140k Real and Fake Faces to accurately detect deepfakes in images with accuracy rates reaching as high as 88.33%.
Related Topics
Concepts
Misinformation
Computer science
Support vector machine
Convolutional neural network
Artificial intelligence
Social media
Machine learning
Fake news
Schema (genetic algorithms)
Face (sociological concept)
Pattern recognition (psychology)
Internet privacy
World Wide Web
Computer security
Social science
Sociology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.22123094.v1
- OA Status
- gold
- Cited By
- 2
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321612390
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4321612390Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.36227/techrxiv.22123094.v1Digital Object Identifier
- Title
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Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVMWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-23Full publication date if available
- Authors
-
Rushit Dave, Mounika vanamalaList of authors in order
- Landing page
-
https://doi.org/10.36227/techrxiv.22123094.v1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.36227/techrxiv.22123094.v1Direct OA link when available
- Concepts
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Misinformation, Computer science, Support vector machine, Convolutional neural network, Artificial intelligence, Social media, Machine learning, Fake news, Schema (genetic algorithms), Face (sociological concept), Pattern recognition (psychology), Internet privacy, World Wide Web, Computer security, Social science, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.publicly | 131 |
| abstract_inverted_index.reaching | 150 |
| abstract_inverted_index.recently | 33 |
| abstract_inverted_index.Deepfakes | 31, 66 |
| abstract_inverted_index.available | 132 |
| abstract_inverted_index.currently | 3 |
| abstract_inverted_index.deepfakes | 92, 144 |
| abstract_inverted_index.detection | 113 |
| abstract_inverted_index.doctored. | 30 |
| abstract_inverted_index.important | 61 |
| abstract_inverted_index.including | 60 |
| abstract_inverted_index.nefarious | 44 |
| abstract_inverted_index.political | 62 |
| abstract_inverted_index.seemingly | 100 |
| abstract_inverted_index.</p> | 155 |
| abstract_inverted_index.accurately | 142 |
| abstract_inverted_index.advancement | 40 |
| abstract_inverted_index.algorithms; | 120 |
| abstract_inverted_index.individuals | 8 |
| abstract_inverted_index.information | 19 |
| abstract_inverted_index.information. | 15 |
| abstract_inverted_index.convolutional | 125 |
| abstract_inverted_index.technological | 39 |
| abstract_inverted_index.<p>Social | 0 |
| abstract_inverted_index.misinformation. | 77 |
| abstract_inverted_index.computer-generated | 53 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.52251253 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |