<p>Deepfake technology, driven by advanced deep learning architectures such as Generative Adversarial Networks (GANs) and autoencoders, has revolutionized digital media creation by enabling the production of highly realistic synthetic images and videos. While these innovations support creative industries and accessibility tools, they also introduce serious risks related to misinformation, identity fraud, and cybersecurity. The growing sophistication of deepfakes poses a threat to public trust in digital content, underscoring the urgent need for reliable and explainable detection mechanisms. This study addresses the problem of evaluating human capability in identifying deepfake facial images and comparing it with the performance of an artificial intelligence (AI)–based detection system. The primary objective is to examine the limitations of human visual perception in recognizing synthetic imagery and to demonstrate how hybrid AI models can surpass human judgment in terms of accuracy and reliability. A controlled experiment was conducted in which participants classified real and fake facial images. At the same time, a hybrid detection model—integrating Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)—was trained and tested on the same dataset. Model performance was measured using standard metrics, including accuracy, precision, recall, and F1-score. The hybrid system achieved a superior detection accuracy of 91.3%, significantly exceeding average human performance. These results reveal that human detection accuracy declines as the quality of manipulation improves, emphasizing the cognitive and perceptual limits of manual deepfake recognition. The findings demonstrate the effectiveness of hybrid AI architectures in enhancing deepfake detection and highlight their essential role in strengthening digital forensics and cybersecurity defenses.</p>