Güncel Deepfake Video Algılama Yöntemleri Üzerine Bir Literatür İncelemesi

Yıl 2024, Cilt: 17 Sayı: 2, 142 - 155, 24.12.2024

Suzan Aydın , Zeki Özen

https://doi.org/10.54525/bbmd.1460699

Öz

Son yıllarda yapay zekâ ve derin öğrenme teknolojilerindeki hızlı gelişmeler, düzmece (Deepfake) gibi yeni ve yenilikçi uygulamaların ortaya çıkmasını sağlamıştır. Düzmece görsel ve işitsel içeriklerin düzenlenmesine olanak tanır ve özellikle bireylerin görüntü ve seslerini taklit etmek için kullanılır. Düzmece teknolojisi sağladığı olanak ve avantajların yanında kişisel bilginin güvenliği, mahremiyeti ve oluşturulan içeriklerin güvenilirliği gibi konularda ciddi endişelere yol açmaktadır. Bu endişeler, Düzmece içeriklerinin algılanması ve doğrulanması amacıyla yapılan araştırmalara ivme kazandırmıştır. Bu kaynak incelemesi, düzmece türlerini, düzmece video içerikleri algılayan algoritmaların eğitiminde kullanılan veri kümelerini ve güncel düzmece video algılama yöntemlerini ele almaktadır

Anahtar Kelimeler

Deepfake, Derin Kurgu, Derin Sahte, Deepfake Türleri, Deepfake Video Algılama Yöntemleri, Derin Öğrenme, Yapay Zekâ

A Literature Review on Current Deepfake Video Detection Methods

Öz

"In recent years, rapid advancements in artificial intelligence and deep learning technologies have led to the emergence of new and innovative applications such as Deepfake. Deepfake allows for the manipulation of visual and auditory content and is particularly used to imitate individuals' images and voices. Alongside the possibilities and advantages provided by Deepfake technology, it raises serious concerns regarding the security of personal information, privacy, and the reliability of the created content. These concerns have accelerated research aimed at the perception and verification of Deepfake content. This literature review addresses types of Deepfake, datasets used in training algorithms that detect Deepfake video content, and current methods for detecting Deepfake videos.

Anahtar Kelimeler

Deepfake, Deepfake Types, Deepfake Video Detection Methods, Deep Learning, Artificial Intelligence

Kaynakça

• Statista. Global internet user penetration 2024, https://0311b0kku-y-https-www-statista-com.halic.proxy.deepknowledge.io/statistics/325706/global-internet-user-penetration/, Erişim tarihi:15.02.2024.
• ITRC. 2023 Annual Data Breach Report, https://www.idtheftcenter.org/publication/2023-data-breach-report/, Erişim tarihi:15.02.2024.
• Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. Deepfake detection using deep learning methods: A systematic and comprehensive review, WIREs Data Mining and Knowledge Discovery, 2023. https://doi.org/10.1002/widm.1520
• Salman, S., Shamsi, J. A., & Qureshi, R., Deep Fake Generation and Detection: Issues, Challenges, and Solutions. IT Professional, 2023, 25(1), 52-59. https://doi.org/10.1109/MITP.2022.3230353
• Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C., Deepfakes: Trick or treat? Business Horizons, 2020, 63(2), 135-146. https://doi.org/10.1016/j.bushor.2019.11.006
• Ajao, O., Bhowmik, D., & Zargari, S., Sentiment Aware Fake News Detection on Online Social Networks, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, 2507-2511. https://doi.org/10.1109/ICASSP.2019.8683170
• Caldelli, R., Galteri, L., Amerini, I., & Bimbo, A., Optical Flow based CNN for detection of unlearnt Deepfake manipulations, Pattern Recognition Letters, 2021, 146. https://doi.org/10.1016/j.patrec.2021.03.005
• Van Der Sloot, B., & Wagensveld, Y., Düzmeces: Regulatory challenges for the synthetic society, Computer Law & Security Review, 2022, 46, 105716. https://doi.org/10.1016/j.clsr.2022.105716
• Neethirajan, S., Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming, Frontiers in Veterinary Science, 2021, 8. https://api.semanticscholar.org/CorpusID:244715980
• Yu, P., Xia, Z., Fei, J., & Lu, Y., A Survey on Deepfake Video Detection, IET Biometrics, 2021, 10(6), 607-624. https://doi.org/10.1049/bme2.12031
• Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y., Generative Adversarial Networks, 2014. https://doi.org/10.48550/ARXIV.1406.2661
• He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X., AttGAN: Facial Attribute Editing by Only Changing What You Want, IEEE Transactions on Image Processing, 2019, 28(11), 5464-5478. https://doi.org/10.1109/TIP.2019.2916751
• Karras, T., Laine, S., & Aila, T., A Style-Based Generator Architecture for Generative Adversarial Networks, 2018. https://doi.org/10.48550/ARXIV.1812.04948
• Liu, M., Ding, Y., Xia, M., Liu, X., Ding, E., Zuo, W., & Wen, S., STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing, 2019. https://doi.org/10.48550/ARXIV.1904.09709
• Choi, Y., Uh, Y., Yoo, J., & Ha, J.-W., StarGAN v2: Diverse Image Synthesis for Multiple Domains, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, 8185-8194. https://doi.org/10.1109/CVPR42600.2020.00821
• Temnikova, I., Marinova, I., Looking for Traces of Textual Deepfakes in Bulgarian on Social Media, Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Processings, 2023, 1151-1161. https://doi.org/10.26615/978-954-452-092-2_122
• Conti, E., Salvi, D., Borrelli, C., Hosler, B., Bestagini, P., Antonacci, F., Sarti, A., Stamm, M. C., & Tubaro, S., Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, 8962-8966. https://doi.org/10.1109/ICASSP43922.2022.9747186
• Patel, Y., Tanwar, S., Bhattacharya, P., Gupta, R., Alsuwian, T., Davidson, I. E., & Mazibuko, T. F., An Improved Dense CNN Architecture for Deepfake Image Detectio, IEEE Access, 2023, 11, 22081-22095. https://doi.org/10.1109/ACCESS.2023.3251417
• Yang, X., Li, Y., & Lyu, S., Exposing Deep Fakes Using Inconsistent Head Poses, 2018. https://doi.org/10.48550/ARXIV.1811.00661
• Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B., Face X-ray for More General Face Forgery Detection, 2019. https://doi.org/10.48550/ARXIV.1912.13458
• Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J., Deepfakes and Beyond: A Survey of Face Manipulation and Fake Detection, 2020. https://doi.org/10.48550/ARXIV.2001.00179
• Zi, B., Chang, M., Chen, J., Ma, X., & Jiang, Y.-G., WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection, Proceedings of the 28th ACM International Conference on Multimedia, 2020.
• Masood, M., Nawaz, M., Malik, K. M., Javed, A., & Irtaza, A., Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward, 2021, https://doi.org/10.48550/ARXIV.2103.00484
• Waseem, S., Abu-Bakar, S., Omar, Z., Ahmed, B., Baloch, S., & Hafeezallah, A., Multi-attention-based approach for Deepfake face and expression swap detection and localization, EURASIP Journal on Image and Video Processing, 2023. https://doi.org/10.1186/s13640-023-00614-z
• Chung, J. S., Nagrani, A., & Zisserman, A., VoxCeleb2: Deep Speaker Recognition, 2018. https://doi.org/10.48550/ARXIV.1806.05622
• Salvi, D., Liu, H., Mandelli, S., Bestagini, P., Zhou, W., Zhang, W., & Tubaro, S., A Robust Approach to Multimodal Deepfake Detection, Journal of Imaging, 2023, 9(6), 122. https://doi.org/10.3390/jimaging9060122
• Gupta, G., Raja, K., Gupta, M., Jan, T., Whiteside, S. T., & Prasad, M., A Comprehensive Review of Deepfake Detection Using Advanced Machine Learning and Fusion Methods, Electronics, 2023, 13(1), 95. https://doi.org/10.3390/electronics13010095
• Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y., Countering Malicious Deepfakes: Survey, Battleground, and Horizon, International Journal of Computer Vision, 2022, 130, 1-57. https://doi.org/10.1007/s11263-022-01606-8
• Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H., Deepfake Detection: A Systematic Literature Review, IEEE Access, 2022, 10, 25494-25513. https://doi.org/10.1109/ACCESS.2022.3154404
• Groh, M., Epstein, Z., Firestone, C., & Picard, R., Deepfake detection by human crowds, machines, and machine-informed crowds, Proceedings of the National Academy of Sciences, 2022, 119(1), e2110013119. https://doi.org/10.1073/pnas.2110013119
• Köbis, N. C., Doležalová, B., & Soraperra, I., Fooled twice: People cannot detect Deepfakes but think they can, iScience, 2021, 24(11), 103364. https://doi.org/10.1016/j.isci.2021.103364
• Somoray, K., & Miller, D. J., Providing detection strategies to improve human detection of Deepfakes: An experimental study, Computers in Human Behavior, 2023, 149, 107917. https://doi.org/10.1016/j.chb.2023.107917
• Deng, L., Deep Learning: Methods and Applications, Foundations and Trends® in Signal Processing, 2014, 7(3-4), 197-387. https://doi.org/10.1561/2000000039
• Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N., Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions, Computers, 2023, 12(10), 216. https://doi.org/10.3390/computers12100216
• Krizhevsky, A., Sutskever, I., & Hinton, G. E., ImageNet classification with deep convolutional neural networks, Communications of the ACM, 2017, 60(6), 84-90. https://doi.org/10.1145/3065386
• Simonyan, K., & Zisserman, A., Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014. https://doi.org/10.48550/ARXIV.1409.1556
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A., Going Deeper with Convolutions, 2014. https://doi.org/10.48550/ARXIV.1409.4842
• He, K., Zhang, X., Ren, S., & Sun, J., Deep Residual Learning for Image Recognition, 2015. https://doi.org/10.48550/ARXIV.1512.03385
• Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q., Densely Connected Convolutional Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 2261-2269. https://doi.org/10.1109/CVPR.2017.243
• Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017. https://doi.org/10.48550/ARXIV.1704.04861
• Tan, M., & Le, Q. V., EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, 2019. https://doi.org/10.48550/ARXIV.1905.11946
• Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I., MesoNet: A Compact Facial Video Forgery Detection Network, 2018. https://doi.org/10.48550/ARXIV.1809.00888
• Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N., Multi-attentional Deepfake Detection, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, 2185-2194. https://doi.org/10.1109/CVPR46437.2021.00222
• Kohli, A., & Gupta, A., Detecting Deepfake, FaceSwap and Face2Face facial forgeries using frequency CNN, Multimedia Tools and Applications, 2021, 80(12), 18461-18478. https://doi.org/10.1007/s11042-020-10420-8
• Luo, Y., Zhang, Y., Yan, J., & Liu, W., Generalizing Face Forgery Detection with High-frequency Features, 2021. https://doi.org/10.48550/ARXIV.2103.12376
• Ismail, A. A., Elpeltagy, M. S., Zaki, M. S., & Eldahshan, K. A., A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost, Sensors, 2021, 21.
• Das, A., & Sebastian, L., A Comparative Analysis and Study of a Fast Parallel CNN Based Deepfake Video Detection Model with Feature Selection (FPC-DFM), 2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), 2023, 1-9. https://doi.org/10.1109/ACCTHPA57160.2023.10083340
• Dhanaraj, R., & Sri̇Devi̇, M., Face Warping Deepfake Detection and Localization in a Digital Video using Transfer Learning Approach, Journal of Metaverse, 2023, 4(1), 11-20. https://doi.org/10.57019/jmv.1338907
• Chollet, F., Xception: Deep Learning with Depthwise Separable Convolutions, 2017. http://arxiv.org/abs/1610.02357
• Matern, F., Riess, C., & Stamminger, M., Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations, 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2019, 83-92. https://doi.org/10.1109/WACVW.2019.00020
• Li, Y., Chang, M.-C., & Lyu, S., In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking, 2018, 1-7. https://doi.org/10.1109/WIFS.2018.8630787
• Jung, T., Kim, S., & Kim, K., DeepVision: Derin Deepfakes Detection Using Human Eye Blinking Pattern, IEEE Access, 2020, 8, 83144-83154. https://doi.org/10.1109/ACCESS.2020.2988660
• Gu, J., Xu, Y., Sun, J., & Liu, W., Exploiting Deepfakes by Analyzing Temporal Feature Inconsistency, International Journal of Advanced Computer Science and Applications, 2023, 14(12). https://doi.org/10.14569/IJACSA.2023.0141291
• He, Q., Peng, C., Liu, D., Wang, N., & Gao, X., GazeForensics: Deepfake Detection via Gaze-guided Spatial Inconsistency Learning, 2023. https://doi.org/10.48550/ARXIV.2311.07075
• Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J., DeepRhythm: Exposing Deepfakes with Attentional Visual Heartbeat Rhythms, 2020. https://doi.org/10.48550/ARXIV.2006.07634
• Ciftci, U., Demir, I., & Yin, L., FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, PP, 1-1. https://doi.org/10.1109/TPAMI.2020.3009287
• Ciftci, U. A., Demir, I., & Yin, L., How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals, 2020. https://doi.org/10.48550/ARXIV.2008.11363
• Hernandez-Ortega, J., Tolosana, R., Fierrez, J., & Morales, A., DeepfakesON-Phys: Deepfakes Detection based on Heart Rate Estimation, 2020. https://doi.org/10.48550/ARXIV.2010.00400
• Wang, B., Li, Y., Wu, X., Ma, Y., Song, Z., & Wu, M., Face Forgery Detection Based on the Improved Siamese Network, Security and Communication Networks, 2022, 1-13. https://doi.org/10.1155/2022/5169873
• Khurana, P. S., Sudarshan, T. B., Natarajan, S., Nagesh, V., Lakshminarayanan, V., Bhat, N., & Vinay, A., AFMB-Net: Deepfake Detection Network Using Heart Rate Analysis, Tehnički glasnik, 2022, 16(4), 503-508. https://doi.org/10.31803/tg-20220403080215
• Liang, P., Liu, G., Xiong, Z., Fan, H., Zhu, H., & Zhang, X., A facial geometry based detection model for face manipulation using CNN-LSTM architecture, Information Sciences, 2023, 633, 370-383. https://doi.org/10.1016/j.ins.2023.03.079
• Zhou, Y., & Lim, S.-N., Joint Audio-Visual Deepfake Detection, 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, 14780-14789. https://doi.org/10.1109/ICCV48922.2021.01453
• Cai, Z., Stefanov, K., Dhall, A., & Hayat, M., Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization, 2022. https://doi.org/10.48550/ARXIV.2204.06228
• Ilyas, H., Javed, A., & Malik, K. M., AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual Deepfakes detection, Applied Soft Computing, 2023, 136, 110124. https://doi.org/10.1016/j.asoc.2023.110124
• Anas Raza, M., & Mahmood Malik, K, Multimodaltrace: Deepfake Detection using Audiovisual Representation Learning, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023, 993-1000. https://doi.org/10.1109/CVPRW59228.2023.00106
• Hashmi, A., Shahzad, S. A., Lin, C.-W., Tsao, Y., & Wang, H.-M., AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection, 2023. https://doi.org/10.48550/ARXIV.2310.13103
• Li, Y., & Lyu, S., Exposing Deepfake Videos By Detecting Face Warping Artifacts, 2018. https://doi.org/10.48550/ARXIV.1811.00656
• Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., & Holz, T., Leveraging Frequency Analysis for Deep Fake Image Recognition, 2020. https://doi.org/10.48550/ARXIV.2003.08685
• Younus, M. A., & Hasan, T. M., Effective and Fast Deepfake Detection Method Based on Haar Wavelet Transform, 2020 International Conference on Computer Science and Software Engineering (CSASE), 2020, 186-190. https://doi.org/10.1109/CSASE48920.2020.9142077
• Huang, Y., Juefei-Xu, F., Guo, Q., Liu, Y., & Pu, G., FakeLocator: Robust Localization of GAN-Based Face Manipulations, 2020. https://doi.org/10.48550/ARXIV.2001.09598
• Xiao, S., Yang, J., & Lv, Z., Protecting the trust and credibility of data by tracking forgery trace based on GANs, Digital Communications and Networks, 2022, 8(6), 877-884. https://doi.org/10.1016/j.dcan.2022.07.010
• Lin, Y.-K., & Sun, H.-L., Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach, Electronics, 2023, 12(6), 1417. https://doi.org/10.3390/electronics12061417
• Agarwal S, Farid H, El-Gaaly T, Lim SN., Detecting deep-fake videos from appearance and behavior, In: 2020 IEEE International Workshop on Information Forensics and Security (WIFS), IEEE, 2020, pp 1–6.
• Agarwal, S., Farid, H., Fried, O., & Agrawala, M., Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 2814-2822. https://doi.org/10.1109/CVPRW50498.2020.00338
• Cozzolino, D., Rössler, A., Thies, J., Nießner, M., & Verdoliva, L., ID-Reveal: Identity-aware Deepfake Video Detection, 2020. https://doi.org/10.48550/ARXIV.2012.02512
• Dong, X., Bao, J., Chen, D., Zhang, T., Zhang, W., Yu, N., Chen, D., Wen, F., & Guo, B., Protecting Celebrities from Deepfake with Identity Consistency Transformer, 2022. https://doi.org/10.48550/ARXIV.2203.01318
• Shen, D., Zhao, Y., & Quan, C., Identity-Referenced Deepfake Detection with Contrastive Learning, Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security, 2022, 27-32. https://doi.org/10.1145/3531536.3532964
• Liu, B., Liu, B., Ding, M., Zhu, T., & Yu, X., TI 2 Net: Temporal Identity Inconsistency Network for Deepfake Detection, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, 4680-4689. https://doi.org/10.1109/WACV56688.2023.00467
• Guera, D., & Delp, E., Deepfake Video Detection Using Recurrent Neural Networks, 2018, 1-6. https://doi.org/10.1109/AVSS.2018.8639163
• Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horváth, J., Bartusiak, E., Yang, J., Güera, D., Zhu, F., & Delp, E. J., Deepfakes Detection with Automatic Face Weighting, 2020. https://doi.org/10.48550/ARXIV.2004.12027
• Zheng, Y., Bao, J., Chen, D., Zeng, M., & Wen, F., Exploring Temporal Coherence for More General Video Face Forgery Detection, 2021. https://doi.org/10.48550/ARXIV.2108.06693
• Saikia, P., Dholaria, D., Yadav, P., Patel, V., & Roy, M., A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features, 2022. https://doi.org/10.48550/ARXIV.2208.00788
• Rahman, A., Siddique, N., Moon, M. J., Tasnim, T., Islam, M., Shahiduzzaman, Md., & Ahmed, S., Short And Low Resolution Deepfake Video Detection Using CNN, 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), 2022, 259-264. https://doi.org/10.1109/R10-HTC54060.2022.9929719
• Kolagati, S., Priyadharshini, T., & Mary Anita Rajam, V., Exposing Deepfake using a deep multilayer perceptron – convolutional neural network model, International Journal of Information Management Data Insights, 2022, 2(1), 100054. https://doi.org/10.1016/j.jjimei.2021.100054
• Thing, V. L. L., Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers, 2023. https://doi.org/10.48550/ARXIV.2304.03698
• Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D., Emotions Don’t Lie: An Audio-Visual Deepfake Detection Method using Affective Cues, Proceedings of the 28th ACM International Conference on Multimedia, 2022, 2823-2832. https://doi.org/10.1145/3394171.3413570
• Hosler, B., Salvi, D., Murray, A., Antonacci, F., Bestagini, P., Tubaro, S., & Stamm, M. C., Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, 1013-1022. https://doi.org/10.1109/CVPRW53098.2021.00112
• Pei, S., Wang, Y., Xiao, B., Pei, S., Xu, Y., Gao, Y., & Zheng, J., A bidirectional-LSTM method based on temporal features for deep fake face detection in videos, 2nd International Conference on Information Technology and Intelligent Control, 2022, 28. https://doi.org/10.1117/12.2653461
• Haq, I. U., Malik, K. M., & Muhammad, K., Multimodal Neurosymbolic Approach for Explainable Deepfake Detection, ACM Transactions on Multimedia Computing, Communications, and Applications, 2023, 3624748. https://doi.org/10.1145/3624748
• Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I., Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos, 2019. https://doi.org/10.48550/ARXIV.1906.06876
• de Lima, O., Franklin, S., Basu, S., Karwoski, B., & George, A., Deepfake Detection using Spatiotemporal Convolutional Networks, 2020. https://doi.org/10.48550/ARXIV.2006.14749
• Li, X., Lang, Y., Chen, Y., Mao, X., He, Y., Wang, S., Xue, H., & Lu, Q., Sharp Multiple Instance Learning for Deepfake Video Detection, 2020. https://doi.org/10.48550/ARXIV.2008.04585
• Hubálovský, Š., Trojovský, P., Bacanin, N., & K, V., Evaluation of deepfake detection using YOLO with local binary pattern histogram, PeerJ Computer Science, 2022, 8, e1086. https://doi.org/10.7717/peerj-cs.1086
• Lu, T., Bao, Y., & Li, L., Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features, Computers, Materials & Continua, 2023, 75(1), 715-740. https://doi.org/10.32604/cmc.2023.034963
• Dolla, M. S., Ruan, L., Zhu, K., & Xiao, L., Spatio-Temporal Feature Pyramid Network for Deepfake Detection, SSRN, 2023. https://doi.org/10.2139/ssrn.4507991
• Kaddar, B., Fezza, S. A., Akhtar, Z., Hamidouche, W., Hadid, A., & Serra-Sagristà, J., Deepfake Detection Using Spatiotemporal Transformer, ACM Transactions on Multimedia Computing, Communications, and Applications, 2024, 3643030. https://doi.org/10.1145/3643030