Beyond the Naked Eye: Why Deepfake Detection is a Data Integrity Problem The technical reality of modern computer vision is shifting. For years, developers in the biometrics and facial recognition space relied on "visual artifacts" as a primary heuristic for detecting manipulation. If the edge of an ear looked fuzzy or the eyes didn't track, the model flagged it. But as recent research in Scientific Reports suggests, we’ve hit a wall: humans are now only 53.5% accurate at spotting deepfakes—essentially a coin flip. For developers building investigative tools, this news means one thing: we must move away from visual-layer analysis and toward deep signal integrity. The Signal vs. The Image When we build facial comparison engines at CaraComp, we focus heavily on Euclidean distance analysis. This isn't about scanning a crowd; it’s about the mathematical relationship between facial landmarks in a side-by-side comparison.…