Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios.

翻译：源自深度学习的深度伪造技术能够将个体无缝插入数字媒体，无论其是否实际参与其中。该技术以机器学习与人工智能为基础。最初，深度伪造服务于研究、工业和娱乐领域。尽管这一概念已存在数十年，但近年来的进步使深度伪造几乎与真实内容难以区分。其易用性大幅提升，即使是新手也能制作出令人信服的深度伪造内容。然而，这种易用性引发了对安全性的担忧。主要的深度伪造生成算法——生成对抗网络（GAN），通过机器学习创建逼真的图像或视频。我们的目标是利用卷积神经网络（CNN）和基于LSTM的胶囊网络（CapsuleNet）来区分深度伪造生成的帧与原始帧。此外，我们旨在通过可解释人工智能阐明模型的决策过程，促进透明的人机交互关系，并为现实场景提供实践案例。