What technical methods do moderation systems use to distinguish real people from fictional characters in image generation?

1. What problem moderators are solving and why it’s hard

Content moderation now must distinguish fabricated faces and manipulated people from genuine photographs because modern generators can mimic not only appearance but lighting and expression, eroding trust in visual media ^{[6] [4]}, and humans routinely fail at this task—the public and lab subjects are often fooled by high-quality synthetic images ^{[2] [7]}.

2. Pixel- and signal-level forensic methods

Traditional forensic techniques look for physical inconsistencies—unnatural textures, mismatched reflections, poor text rendering, or anatomical distortions—that betray many AI images ^{[8] [9]}, and inpainting or partial-edit techniques can be flagged by detecting seams where generated pixels blend with genuine ones ^[3]; however, these cues become unreliable as generators improve ^{[3] [2]}.

3. Model-based detectors and adversarial networks

Automated detectors are themselves machine-learned classifiers trained to spot patterns left by generators (for example, artifacts from GAN pipelines), and research has used discriminators and specialized convolutional nets to catch synthetic faces ^{[1] [10]}, but these detectors face model generalization problems when new generators or prompt-engineered outputs produce different artifacts, making the detection task a shifting target ^{[2] [3]}.

4. Metadata, watermarking and provenance signals

Because pixel forensics alone is fragile, platforms and researchers promote provenance approaches such as cryptographic metadata, digital watermarks, and signed “origin” records to certify images; absence of a watermark or provenance trail can be used to raise suspicion while visible or robust watermarks enable swift classification ^{[3] [5]}. Public reporting notes, however, that many outputs lack detectable watermarks and that watermarking requires industry coordination to be effective ^{[3] [5]}.

5. Policy-engineering: blocking edits and usage rules

Moderation systems combine technical detection with policy rules—blocking photo-based edits of real people, forbidding sexualized or deceptive imagery, and restricting upload-to-edit workflows—to reduce risk even where detection is imperfect, as seen in platform rule changes limiting photo-to-art transformations involving real people ^{[5] [6]}.

6. Human review, workflow orchestration and scaling limits

Automated systems triage at scale but escalate borderline or high-risk content to human moderators; studies and industry guides stress humans are necessary yet overwhelmed and prone to error, which creates a workflow dependency that is costly and slow relative to the speed of image generation ^{[2] [6]}. This hybrid model mitigates some false positives and negatives but cannot eliminate the arms-race dynamics between detectors and generators ^{[2] [3]}.

7. The adversarial arms race and remaining gaps

Detection methods that rely on identifying generator artifacts, metadata gaps, or visual inconsistencies are all subject to circumvention: generators learn to avoid artifacts, provenance can be stripped or forged, and small targeted edits (inpainting or blended composites) can insert AI elements into otherwise genuine photos to evade detectors ^{[3] [2] [1]}. Research showing AI can create matched synthetic versions of familiar faces underscores the urgency for multi-layered solutions and continual updating of detection models ^{[4] [7]}.

Conclusion: layered defenses, not a silver bullet

The technical answer is that moderation uses complementary signals—forensic pixel analysis, trained detectors, provenance/watermarks, and human review—stitched together with policy controls to judge whether an image depicts a real person or a fictional one; each method is useful but brittle, and the literature and industry reporting make clear that no single method currently guarantees reliable separation at scale ^{[1] [2] [3] [5]}.