ANALISIS GAMBAR WAJAH PALSU: MENDETEKSI KEASLIAN GAMBAR YANG DIMANIPULASI MENGGUNAKAN METODE VARIATIONAL AUTOENCODER DAN FORENSICS DEEP NEURAL NETWORK
Main Article Content
Regina Angelika Septi Rahayu
Handri Santoso
Artificial intelligence (AI) is one of the technologies commonly used for automated computer systems. Artificial intelligence is designed to solve cognitive problems. The convenience provided by AI is sometimes misused, resulting in negative impacts on many people. One negative impact of AI technology misuse is deepfake. Deepfake is a technology used for image or video manipulation. The manipulation techniques used in deepfake are employed to alter images, such as faces, places, objects or even voices. Variational autoencoder (VAE) is a deep learning algorithm that can be used for facial manipulation. The result of the VAE process is an image obtained from the merging of original facial images during the training process. The new facial images generated from VAE training are called decoder images or manipulations. Both original facial images and manipulation facial images will be analyzed using the forensics deep neural network method. The analysis technique involves the use of error level analysis (ELA), which helps identify significant changes that occur in the images. Based on the testing results using both original and manipulated facial images, the applied method demonstrates the ability to detect real and fake images.
Candra, P. N., & Prapanca, A. (2020). Klasifikasi Gambar Asli dan Manipulasi Menggunakan Error Level Analysis (ELA) Sebagai Proses Komputasi Metode Convolutional Neural Network (CNN). Journal of Informatics and Computer Science (JINACS), 2(01), 9–18. https://doi.org/10.26740/jinacs.v2n01.p9-18
Das, A., Viji, K. S. A., & Sebastian, L. (2022, July 29). A survey on deepfake video detection techniques using deep learning. 2022 Second International Conference on Next Generation Intelligent Systems (ICNGIS). http://dx.doi.org/10.1109/icngis54955.2022.10079802
Das, S., Seferbekov, S., Datta, A., Islam, Md. S., & Amin, Md. R. (2021, October). Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). http://dx.doi.org/10.1109/iccvw54120.2021.00421
Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014, June). Scalable object detection using deep neural networks. 2014 IEEE Conference on Computer Vision and Pattern Recognition. http://dx.doi.org/10.1109/cvpr.2014.276
Foster, D. (2019). Generative deep learning: Teaching machines to paint, write, compose, and play. “O’Reilly Media, Inc.”
Keras: The high-level API for TensorFlow. (n.d.). TensorFlow. Retrieved June 25, 2023, from https://www.tensorflow.org/guide/keras
Kim, E., & Cho, S. (2021). Exposing fake faces through deep neural networks combining content and trace feature extractors. IEEE Access, 9, 123493–123503. https://doi.org/10.1109/access.2021.3110859
Li, J. (n.d.). CelebFaces attributes (celeba) dataset. Kaggle. Retrieved June 25, 2023, from https://www.kaggle.com/jessicali9530/celeba-dataset
Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022). DeepFake detection for human face images and videos: A survey. IEEE Access, 10, 18757–18775. https://doi.org/10.1109/access.2022.3151186
shaft49. (2020, September 9). real vs fake images (casia dataset). Kaggle. https://www.kaggle.com/code/shaft49/real-vs-fake-images-casia-dataset
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020). Deepfakes and beyond: A Survey of face manipulation and fake detection. Information Fusion, 64, 131–148. https://doi.org/10.1016/j.inffus.2020.06.014
Yavuzkilic, S., Sengur, A., Akhtar, Z., & Siddique, K. (2021). Spotting deepfakes and face manipulations by fusing features from multi-stream cnns models. Symmetry, 13(8), 1352. https://doi.org/10.3390/sym13081352
