Source: NewsGuard's Reality Check
The gap between lab performance and real-world accuracy in deepfake detection has become a liability for platforms attempting to moderate synthetic media at scale. Tools trained on controlled datasets routinely misidentify authentic images or miss sophisticated fakes, pushing moderation work back onto human reviewers who lack consistent protocols. As bad actors iterate faster than detection vendors can update their models, the tools function more as theater than infrastructure, giving publishers and platforms cover to claim they're "detecting AI" while the actual labor falls to underpaid content moderators making judgment calls on ambiguous artifacts. Detection-first approaches assume authentication is primarily a technical problem. The actual bottleneck is establishing provenance and context at the point of creation—something no image classifier can accomplish alone.