Deepfake attribution models struggle with generalization because they typically learn artifact patterns specific to the generation method, model version, and training data used to create the fakes they were trained on. A classifier that achieves 98% accuracy detecting Deepfakes generated by StyleGAN2 in 2021 might drop to 65% accuracy on content from Stable Diffusion or newer GAN variants released six months later. The core problem is that these models latch onto incidental fingerprints rather than fundamental properties of synthetic media. The generalization gap becomes critical in three scenarios. First, cross-architecture detection fails when models encounter synthesis methods not represented in training data. Second, temporal drift occurs as deepfake tools evolve and old detection signatures become obsolete. Third, adversarial evasion happens when bad actors specifically tune their generation pipeline to evade known detectors by post-processing outputs or using ensemble approaches. For analytics and tracking purposes, this creates measurement challenges. If you're monitoring synthetic content across a domain portfolio or tracking manipulation attempts, a single attribution model won't catch everything. You need ensemble approaches that combine multiple detection signals: physiological inconsistencies like irregular blinking, optical flow anomalies, frequency domain analysis, and metadata forensics. Even then, expect 15-25% false negative rates on novel generation methods. At Ottawa SEO, when we audit sites for synthetic content risks especially for reputation management or competitor analysis we layer behavioral signals with technical detection. A face-swap video might fool image classifiers but still exhibits unnatural head poses, inconsistent lighting across frames, or temporal jitter that multi-modal analysis catches. We also watch for metadata clues and publishing patterns rather than relying solely on pixel-level attribution. The practical takeaway: budget for continuous model retraining if detection accuracy matters to your use case. Attribution models degrade like spam filters, requiring quarterly updates as synthesis techniques evolve. Treat detection confidence scores as probabilistic rather than binary, and always combine automated detection with human review for high-stakes decisions.