Temporal Feature Prediction in Audio–Visual Deepfake Detection Article Swipe
Yuan Gao
,
Xuelong Wang
,
Yu Zhang
,
Ping Zeng
,
Yingjie Ma
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13173433
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13173433
The rapid growth of deepfake technology, generating realistic manipulated media, poses a significant threat due to potential misuse. Therefore, effective detection methods are urgently needed to prevent malicious use, as current approaches often focus on single modalities or the simple fusion of audio–visual signals, limiting their accuracy. To solve this problem, we propose a deepfake detection scheme based on bimodal temporal feature prediction, which innovatively introduces the idea of temporal feature prediction into the audio–video bimodal deepfake detection task, aiming at fully exploiting the temporal laws of audio–visual modalities. First, pairs of adjacent audio–video sequence clips are used to construct input quadruples, and a dual-stream network is employed to extract temporal feature representations from video and audio, respectively. A video prediction module and an audio prediction module are designed to capture the temporal inconsistencies within each single modality by predicting future temporal features and comparing them with reference features. Then, a projection layer network is designed to align the audio–visual features, using contrastive loss functions to perform contrastive learning and maximize the differences between real and fake video modalities. Experiments on the FakeAVCeleb dataset demonstrate superior performance with an accuracy of 84.33% and an AUC of 89.91%, outperforming existing methods and confirming the effectiveness of our approach in deepfake detection.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13173433
- OA Status
- gold
- Cited By
- 9
- References
- 42
- Related Works
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- OpenAlex ID
- https://openalex.org/W4401991736
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https://openalex.org/W4401991736Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/electronics13173433Digital Object Identifier
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Temporal Feature Prediction in Audio–Visual Deepfake DetectionWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-29Full publication date if available
- Authors
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Yuan Gao, Xuelong Wang, Yu Zhang, Ping Zeng, Yingjie MaList of authors in order
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https://doi.org/10.3390/electronics13173433Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/electronics13173433Direct OA link when available
- Concepts
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Feature (linguistics), Audio visual, Computer science, Pattern recognition (psychology), Artificial intelligence, Multimedia, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 6, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-08-29 |
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| referenced_works | https://openalex.org/W2942074357, https://openalex.org/W3034900344, https://openalex.org/W6780316881, https://openalex.org/W3034196597, https://openalex.org/W4214680478, https://openalex.org/W3094728142, https://openalex.org/W3176241004, https://openalex.org/W3183999072, https://openalex.org/W6810868502, https://openalex.org/W6794693742, https://openalex.org/W4322576470, https://openalex.org/W3175342695, https://openalex.org/W4386072334, https://openalex.org/W4398789263, https://openalex.org/W4381140704, https://openalex.org/W4360993864, https://openalex.org/W4385805162, https://openalex.org/W4392942875, https://openalex.org/W4214691743, https://openalex.org/W4320882980, https://openalex.org/W3093010840, https://openalex.org/W3093077034, https://openalex.org/W4312472072, https://openalex.org/W4386267173, https://openalex.org/W6810653054, https://openalex.org/W4385801058, https://openalex.org/W4280577432, https://openalex.org/W4391799307, https://openalex.org/W4367016785, https://openalex.org/W2963307811, https://openalex.org/W2581625266, https://openalex.org/W2984287396, https://openalex.org/W2194775991, https://openalex.org/W6739901393, https://openalex.org/W2982058372, https://openalex.org/W3174508664, https://openalex.org/W4226024856, https://openalex.org/W4386928847, https://openalex.org/W4401413990, https://openalex.org/W3037391061, https://openalex.org/W4385245566, https://openalex.org/W3158353280 |
| referenced_works_count | 42 |
| abstract_inverted_index.A | 118 |
| abstract_inverted_index.a | 11, 53, 103, 150 |
| abstract_inverted_index.To | 47 |
| abstract_inverted_index.an | 123, 188, 193 |
| abstract_inverted_index.as | 29 |
| abstract_inverted_index.at | 80 |
| abstract_inverted_index.by | 138 |
| abstract_inverted_index.in | 207 |
| abstract_inverted_index.is | 106, 154 |
| abstract_inverted_index.of | 3, 41, 68, 86, 91, 190, 195, 204 |
| abstract_inverted_index.on | 34, 58, 180 |
| abstract_inverted_index.or | 37 |
| abstract_inverted_index.to | 15, 25, 98, 108, 129, 156, 165 |
| abstract_inverted_index.we | 51 |
| abstract_inverted_index.AUC | 194 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 102, 115, 122, 143, 169, 175, 192, 200 |
| abstract_inverted_index.are | 22, 96, 127 |
| abstract_inverted_index.due | 14 |
| abstract_inverted_index.our | 205 |
| abstract_inverted_index.the | 38, 66, 73, 83, 131, 158, 171, 181, 202 |
| abstract_inverted_index.each | 135 |
| abstract_inverted_index.fake | 176 |
| abstract_inverted_index.from | 113 |
| abstract_inverted_index.idea | 67 |
| abstract_inverted_index.into | 72 |
| abstract_inverted_index.laws | 85 |
| abstract_inverted_index.loss | 163 |
| abstract_inverted_index.real | 174 |
| abstract_inverted_index.them | 145 |
| abstract_inverted_index.this | 49 |
| abstract_inverted_index.use, | 28 |
| abstract_inverted_index.used | 97 |
| abstract_inverted_index.with | 146, 187 |
| abstract_inverted_index.Then, | 149 |
| abstract_inverted_index.align | 157 |
| abstract_inverted_index.audio | 124 |
| abstract_inverted_index.based | 57 |
| abstract_inverted_index.clips | 95 |
| abstract_inverted_index.focus | 33 |
| abstract_inverted_index.fully | 81 |
| abstract_inverted_index.input | 100 |
| abstract_inverted_index.layer | 152 |
| abstract_inverted_index.often | 32 |
| abstract_inverted_index.pairs | 90 |
| abstract_inverted_index.poses | 10 |
| abstract_inverted_index.rapid | 1 |
| abstract_inverted_index.solve | 48 |
| abstract_inverted_index.task, | 78 |
| abstract_inverted_index.their | 45 |
| abstract_inverted_index.using | 161 |
| abstract_inverted_index.video | 114, 119, 177 |
| abstract_inverted_index.which | 63 |
| abstract_inverted_index.84.33% | 191 |
| abstract_inverted_index.First, | 89 |
| abstract_inverted_index.aiming | 79 |
| abstract_inverted_index.audio, | 116 |
| abstract_inverted_index.fusion | 40 |
| abstract_inverted_index.future | 140 |
| abstract_inverted_index.growth | 2 |
| abstract_inverted_index.media, | 9 |
| abstract_inverted_index.module | 121, 126 |
| abstract_inverted_index.needed | 24 |
| abstract_inverted_index.scheme | 56 |
| abstract_inverted_index.simple | 39 |
| abstract_inverted_index.single | 35, 136 |
| abstract_inverted_index.threat | 13 |
| abstract_inverted_index.within | 134 |
| abstract_inverted_index.89.91%, | 196 |
| abstract_inverted_index.between | 173 |
| abstract_inverted_index.bimodal | 59, 75 |
| abstract_inverted_index.capture | 130 |
| abstract_inverted_index.current | 30 |
| abstract_inverted_index.dataset | 183 |
| abstract_inverted_index.extract | 109 |
| abstract_inverted_index.feature | 61, 70, 111 |
| abstract_inverted_index.methods | 21, 199 |
| abstract_inverted_index.misuse. | 17 |
| abstract_inverted_index.network | 105, 153 |
| abstract_inverted_index.perform | 166 |
| abstract_inverted_index.prevent | 26 |
| abstract_inverted_index.propose | 52 |
| abstract_inverted_index.accuracy | 189 |
| abstract_inverted_index.adjacent | 92 |
| abstract_inverted_index.approach | 206 |
| abstract_inverted_index.deepfake | 4, 54, 76, 208 |
| abstract_inverted_index.designed | 128, 155 |
| abstract_inverted_index.employed | 107 |
| abstract_inverted_index.existing | 198 |
| abstract_inverted_index.features | 142 |
| abstract_inverted_index.learning | 168 |
| abstract_inverted_index.limiting | 44 |
| abstract_inverted_index.maximize | 170 |
| abstract_inverted_index.modality | 137 |
| abstract_inverted_index.problem, | 50 |
| abstract_inverted_index.sequence | 94 |
| abstract_inverted_index.signals, | 43 |
| abstract_inverted_index.superior | 185 |
| abstract_inverted_index.temporal | 60, 69, 84, 110, 132, 141 |
| abstract_inverted_index.urgently | 23 |
| abstract_inverted_index.accuracy. | 46 |
| abstract_inverted_index.comparing | 144 |
| abstract_inverted_index.construct | 99 |
| abstract_inverted_index.detection | 20, 55, 77 |
| abstract_inverted_index.effective | 19 |
| abstract_inverted_index.features, | 160 |
| abstract_inverted_index.features. | 148 |
| abstract_inverted_index.functions | 164 |
| abstract_inverted_index.malicious | 27 |
| abstract_inverted_index.potential | 16 |
| abstract_inverted_index.realistic | 7 |
| abstract_inverted_index.reference | 147 |
| abstract_inverted_index.Therefore, | 18 |
| abstract_inverted_index.approaches | 31 |
| abstract_inverted_index.confirming | 201 |
| abstract_inverted_index.detection. | 209 |
| abstract_inverted_index.exploiting | 82 |
| abstract_inverted_index.generating | 6 |
| abstract_inverted_index.introduces | 65 |
| abstract_inverted_index.modalities | 36 |
| abstract_inverted_index.predicting | 139 |
| abstract_inverted_index.prediction | 71, 120, 125 |
| abstract_inverted_index.projection | 151 |
| abstract_inverted_index.Experiments | 179 |
| abstract_inverted_index.FakeAVCeleb | 182 |
| abstract_inverted_index.contrastive | 162, 167 |
| abstract_inverted_index.demonstrate | 184 |
| abstract_inverted_index.differences | 172 |
| abstract_inverted_index.dual-stream | 104 |
| abstract_inverted_index.manipulated | 8 |
| abstract_inverted_index.modalities. | 88, 178 |
| abstract_inverted_index.performance | 186 |
| abstract_inverted_index.prediction, | 62 |
| abstract_inverted_index.quadruples, | 101 |
| abstract_inverted_index.significant | 12 |
| abstract_inverted_index.technology, | 5 |
| abstract_inverted_index.innovatively | 64 |
| abstract_inverted_index.audio–video | 74, 93 |
| abstract_inverted_index.effectiveness | 203 |
| abstract_inverted_index.outperforming | 197 |
| abstract_inverted_index.respectively. | 117 |
| abstract_inverted_index.audio–visual | 42, 87, 159 |
| abstract_inverted_index.inconsistencies | 133 |
| abstract_inverted_index.representations | 112 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.92773755 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |