## 1 Introduction  
Deep learning, a subfield of artificial intelligence, began its foundational development in the 1950s and has gained significant momentum in recent years due to the increasing availability of data and advancements in hardware technology. Neural networks with deeper layers have enabled effective modeling of complex datasets, achieving results that surpass human capabilities in areas like image processing, natural language processing, and autonomous systems. These advancements have positioned deep learning at the forefront of artificial intelligence research and technological innovation. Deepfakes, a specific application of deep learning, have emerged as a critical concern due to their potential to disseminate misinformation and undermine democratic processes. By integrating fake facial elements into real footage, these technologies can create highly convincing manipulated videos. Initially popularized in entertainment, deepfakes have raised significant concerns for journalism and public trust when used maliciously in misinformation campaigns [1]. Deep learning techniques such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) are essential for both the creation and detection of fake videos. Detection efforts often rely on identifying spatial and temporal inconsistencies, such as unnatural blinking patterns or misaligned facial movements [2]. However, despite their effectiveness, current methods face significant challenges, including the need for real-time detection systems, limited dataset diversity, and the rapid evolution of deepfake generation techniques. Although many approaches perform well in controlled settings, their applicability in real-world scenarios remains limited, highlighting the need for more robust solutions [3]. Quantum computing, leveraging quantum bits (qubits), offers transformative capabilities for solving complex problems beyond the reach of classical systems. By utilizing principles such as superposition and entanglement, quantum systems have potential applications in fields ranging from material science to cryptography and drug discovery. Quantum computing's integration with artificial intelligence has given rise to quantum machine learning (QML), enabling more efficient data processing and innovative algorithm development [4]. Quantum machine learning combines classical machine learning techniques with quantum computational advantages to enhance the processing of high-dimensional data. Tools such as Parametric Quantum Circuits (PQCs) address classical computational limitations, offering novel solutions for emerging AI challenges [5]. In the literature, several significant studies have been conducted to improve deepfake detection. [PERSON] et al. (2024) developed a model for video-based deepfake detection guided by facial feature adaptation. Based on the CLIP model, this method achieved a 0.9% AUROC improvement in cross-dataset evaluations and a 4.4% accuracy increase on the DFDC dataset. The study demonstrated consistent performance in deepfake detection by optimizing foundational models based on facial features [6]. [PERSON] et al. (2020) introduced an innovative model based on convolutional traces aimed at detecting manipulated images. By utilizing convolutional features for facial data analysis, this method achieved an accuracy of 87%. The study presented a simple yet effective approach for forgery detection [7]. [PERSON] et al. (2021) developed a deepfake detection model based on multi-attention mechanisms. This model was tested across various datasets, achieving 89.5% accuracy and a 0.92 AUROC. The study successfully highlighted the impact of attention mechanisms in deepfake detection [8]. [PERSON] et al. (2021) designed a deepfake detection method combining visual and audio data. By integrating visual and audio analyses using the ResNet and Swin Transformer architectures, this approach achieved 92.1% accuracy on the DFDC dataset, offering an effective solution for deepfake detection [9]. [PERSON] et al.