Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext
Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Fakultas Ilmu Komputer UMI
2022-12-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1254 |
| _version_ | 1797850439749730304 |
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| author | Muhammad Indra Abidin Ingrid Nurtanio Andani Achmad |
| author_facet | Muhammad Indra Abidin Ingrid Nurtanio Andani Achmad |
| author_sort | Muhammad Indra Abidin |
| collection | DOAJ |
| description | Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small. |
| first_indexed | 2024-04-09T19:00:16Z |
| format | Article |
| id | doaj.art-746e690f4ffe469b95242e6b42f98823 |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-09T19:00:16Z |
| publishDate | 2022-12-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-746e690f4ffe469b95242e6b42f988232023-04-08T08:20:29ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792022-12-0114317818510.33096/ilkom.v14i3.1254.178-185449Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNextMuhammad Indra Abidin0Ingrid Nurtanio1Andani Achmad2Universitas HasanuddinUniversitas HasanuddinUniversitas HasanuddinDeep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1254deepfakeresnext cnnlstm |
| spellingShingle | Muhammad Indra Abidin Ingrid Nurtanio Andani Achmad Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext Ilkom Jurnal Ilmiah deepfake resnext cnn lstm |
| title | Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext |
| title_full | Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext |
| title_fullStr | Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext |
| title_full_unstemmed | Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext |
| title_short | Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext |
| title_sort | deepfake detection in videos using long short term memory and cnn resnext |
| topic | deepfake resnext cnn lstm |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1254 |
| work_keys_str_mv | AT muhammadindraabidin deepfakedetectioninvideosusinglongshorttermmemoryandcnnresnext AT ingridnurtanio deepfakedetectioninvideosusinglongshorttermmemoryandcnnresnext AT andaniachmad deepfakedetectioninvideosusinglongshorttermmemoryandcnnresnext |