Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques Article Swipe
Premanand Ghadekar
,
Vaibhavi Shetty
,
Prapti Maheshwari
,
Raj J. Shah
,
Anish Shaha
,
Vaishnav Sonawane
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.46604/peti.2023.10290
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.46604/peti.2023.10290
Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a ResNet-34 pre-trained model and the transfer learning approach, has been achieved. The proposed model achieves an accuracy of 99% with 0.3% validation loss on the validation part of the logical access dataset, which is better than earlier models in the range of 90-95% accuracy on the validation set.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.46604/peti.2023.10290
- https://ojs.imeti.org/index.php/PETI/article/download/10290/1332
- OA Status
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- References
- 26
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4313326425Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.46604/peti.2023.10290Digital Object Identifier
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Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning TechniquesWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-12-29Full publication date if available
- Authors
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Premanand Ghadekar, Vaibhavi Shetty, Prapti Maheshwari, Raj J. Shah, Anish Shaha, Vaishnav SonawaneList of authors in order
- Landing page
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https://doi.org/10.46604/peti.2023.10290Publisher landing page
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https://ojs.imeti.org/index.php/PETI/article/download/10290/1332Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.imeti.org/index.php/PETI/article/download/10290/1332Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Digital forensics, Transfer of learning, Deep learning, Set (abstract data type), Identification (biology), Digital content, Software, Multimedia, Pattern recognition (psychology), Machine learning, Computer security, Programming language, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 9 |
| abstract_inverted_index.has | 102 |
| abstract_inverted_index.the | 20, 43, 48, 61, 83, 87, 98, 118, 122, 133, 139 |
| abstract_inverted_index.use | 22 |
| abstract_inverted_index.0.3% | 114 |
| abstract_inverted_index.aims | 39 |
| abstract_inverted_index.been | 103 |
| abstract_inverted_index.boon | 8 |
| abstract_inverted_index.edit | 12 |
| abstract_inverted_index.fake | 53 |
| abstract_inverted_index.loss | 116 |
| abstract_inverted_index.part | 120 |
| abstract_inverted_index.set. | 141 |
| abstract_inverted_index.such | 24 |
| abstract_inverted_index.than | 129 |
| abstract_inverted_index.this | 37 |
| abstract_inverted_index.used | 66 |
| abstract_inverted_index.with | 76, 113 |
| abstract_inverted_index.Also, | 86 |
| abstract_inverted_index.audio | 16, 46, 90 |
| abstract_inverted_index.build | 68 |
| abstract_inverted_index.given | 49 |
| abstract_inverted_index.model | 70, 96, 107 |
| abstract_inverted_index.range | 134 |
| abstract_inverted_index.real. | 55 |
| abstract_inverted_index.study | 38 |
| abstract_inverted_index.using | 92 |
| abstract_inverted_index.video | 14, 44, 58 |
| abstract_inverted_index.which | 71, 126 |
| abstract_inverted_index.90-95% | 136 |
| abstract_inverted_index.Hence, | 36 |
| abstract_inverted_index.access | 124 |
| abstract_inverted_index.better | 128 |
| abstract_inverted_index.layers | 64 |
| abstract_inverted_index.models | 131 |
| abstract_inverted_index.people | 10 |
| abstract_inverted_index.tools, | 28 |
| abstract_inverted_index.Digital | 0 |
| abstract_inverted_index.average | 78 |
| abstract_inverted_index.content | 1, 51 |
| abstract_inverted_index.digital | 29, 50 |
| abstract_inverted_index.earlier | 130 |
| abstract_inverted_index.forgery | 59 |
| abstract_inverted_index.logical | 123 |
| abstract_inverted_index.prevent | 19 |
| abstract_inverted_index.readily | 25 |
| abstract_inverted_index.whether | 42 |
| abstract_inverted_index.working | 5 |
| abstract_inverted_index.accuracy | 79, 110, 137 |
| abstract_inverted_index.achieves | 108 |
| abstract_inverted_index.altering | 27 |
| abstract_inverted_index.becoming | 33 |
| abstract_inverted_index.content. | 17 |
| abstract_inverted_index.dataset, | 125 |
| abstract_inverted_index.dataset. | 85 |
| abstract_inverted_index.forgery, | 91 |
| abstract_inverted_index.identify | 41, 73 |
| abstract_inverted_index.learning | 100 |
| abstract_inverted_index.proposed | 106 |
| abstract_inverted_index.recorded | 13 |
| abstract_inverted_index.software | 3 |
| abstract_inverted_index.temporal | 57, 74 |
| abstract_inverted_index.transfer | 99 |
| abstract_inverted_index.ResNet-34 | 94 |
| abstract_inverted_index.achieved. | 104 |
| abstract_inverted_index.approach, | 101 |
| abstract_inverted_index.available | 26 |
| abstract_inverted_index.forensics | 31 |
| abstract_inverted_index.forgeries | 75 |
| abstract_inverted_index.unethical | 21 |
| abstract_inverted_index.detection, | 60 |
| abstract_inverted_index.important. | 35 |
| abstract_inverted_index.multimedia | 30 |
| abstract_inverted_index.validation | 84, 115, 119, 140 |
| abstract_inverted_index.pre-trained | 95 |
| abstract_inverted_index.increasingly | 34 |
| abstract_inverted_index.manipulation | 2 |
| abstract_inverted_index.convolutional | 62 |
| abstract_inverted_index.identification | 88 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.16272718 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |