<![CDATA[The rapid growth of generative artificial intelligence has increased the spread of deephoax images, creating significant challenges for digital security, public trust, and the reliability of online information. This study evaluates the performance of MobileNet and Xception architectures for classifying facial images into fact and hoax categories while analyzing different feature representation levels within the Xception architecture through an ablation-based fine-tuning strategy. The proposed framework consists of data preparation, model architecture design, training procedures, and performance evaluation using confusion matrix analysis and metrics such as accuracy, precision, recall, and F1-score. Two publicly available datasets, DeepDetect2025 and FF-GenAI were utilized to evaluate the robustness of the proposed models. Experimental results show that Xception consistently outperformed MobileNet across all evaluation metrics, with the Middle Level Layer configuration achieving the best performance at 99.71% accuracy and F1-score. The findings indicate that intermediate feature representations are the most effective for capturing structural inconsistencies, texture irregularities, and synthetic blending artifacts commonly found in AI-generated facial images. In contrast, low-level representations were less discriminative, while highly abstract semantic representations slightly reduced sensitivity to localized manipulation artifacts. Overall, this study demonstrates the effectiveness of Xception-based feature refinement for deephoax image detection and contributes to AI-based approaches for digital content verification, cybersecurity, and visual misinformation mitigation. ]]>
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