Deepfake technology presents critical cybersecurity challenges that have become more popular since easily accessible applications have become more widely available. The proliferation of fake portrait videos constitutes a serious risk to the legal system, society, and per sonal privacy. The publication of fraudulent explicit content starring celebrities, the circulation of fake political videos, and the use of faked impersonated videos as proof in court of law are all examples of the ef fects of deepfakes in the real-world. In reaction to this growing threat, we propose a simple yet efficient Siamese network-based model to detect deepfake synthetic content in portrait images, providing a pre ventative measure against the growing danger of deepfakes. Unlike the traditional existing neural networks-based methods, which process inputs independently to detect deepfake images, our Siamese network-based proposed method processes a pair of two input images simultaneously using two identical sub-networks that share the same weights and parameters. This connection allows the sub-networks to process two different inputs in a consistent manner, making it possible to compare the inputs effectively