The paper "Deepfakes: current and future trends" by Ángel Fernández Gambín, Anis Yazidi, Athanasios Vasilakos, Hårek Haugerud, and Youcef Djenouri provides a comprehensive overview of the deepfake paradigm, focusing on both current and future trends. The authors highlight the severe threats posed by deepfakes, including public opinion manipulation, geopolitical tensions, financial market chaos, scams, defamation, and identity theft. They emphasize the need for techniques to prevent, detect, and stop the spread of deepfake content. The paper begins with an introduction to the digital era and the exponential growth of information and communication technologies (ICT), which has transformed society and increased the amount of online data. It discusses the challenges of discerning truth and establishing trust in information, noting that human capacity to detect deception is limited. The authors then define deepfakes as the generation or manipulation of digital content using Deep Learning (DL) techniques, particularly focusing on facial appearance manipulation. The paper reviews the state of DL techniques used for deepfakes, including General Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders. It also discusses the ongoing battle between generation and detection techniques, highlighting the need for more robust and generalizable models. The authors explore the potential of new technologies, such as distributed ledgers and blockchain, in combating digital deception. They review several applications of blockchain in content authentication and digital fraud detection, emphasizing the need for further research and practical implementations. Two specific scenarios are analyzed: online social networks and Internet of Things (IoT) networks. The paper discusses the impact of deepfakes on these contexts, including the potential for social engineering attacks and cybersecurity issues in IoT. Finally, the paper concludes by discussing future trends and research directions, emphasizing the importance of multidisciplinary collaborations and the integration of AI and blockchain technologies to address the challenges posed by deepfakes. The authors aim to provide a comprehensive perspective on the deepfake paradigm, offering insights and guidance for further research and practical applications.The paper "Deepfakes: current and future trends" by Ángel Fernández Gambín, Anis Yazidi, Athanasios Vasilakos, Hårek Haugerud, and Youcef Djenouri provides a comprehensive overview of the deepfake paradigm, focusing on both current and future trends. The authors highlight the severe threats posed by deepfakes, including public opinion manipulation, geopolitical tensions, financial market chaos, scams, defamation, and identity theft. They emphasize the need for techniques to prevent, detect, and stop the spread of deepfake content. The paper begins with an introduction to the digital era and the exponential growth of information and communication technologies (ICT), which has transformed society and increased the amount of online data. It discusses the challenges of discerning truth and establishing trust in information, noting that human capacity to detect deception is limited. The authors then define deepfakes as the generation or manipulation of digital content using Deep Learning (DL) techniques, particularly focusing on facial appearance manipulation. The paper reviews the state of DL techniques used for deepfakes, including General Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders. It also discusses the ongoing battle between generation and detection techniques, highlighting the need for more robust and generalizable models. The authors explore the potential of new technologies, such as distributed ledgers and blockchain, in combating digital deception. They review several applications of blockchain in content authentication and digital fraud detection, emphasizing the need for further research and practical implementations. Two specific scenarios are analyzed: online social networks and Internet of Things (IoT) networks. The paper discusses the impact of deepfakes on these contexts, including the potential for social engineering attacks and cybersecurity issues in IoT. Finally, the paper concludes by discussing future trends and research directions, emphasizing the importance of multidisciplinary collaborations and the integration of AI and blockchain technologies to address the challenges posed by deepfakes. The authors aim to provide a comprehensive perspective on the deepfake paradigm, offering insights and guidance for further research and practical applications.