Features / Authenticity Detection
Know which submissions are actually real.
AI-generated photos and video are showing up in contest and UGC submissions at a scale no moderation queue was built to catch by eye alone. NewSpark trains a content authenticity model on your own moderators' real/fake decisions — not a generic, one-size-fits-all classifier that's never seen your campaign — so flagging gets sharper the more your team uses it.
A model that learns your submissions, not the whole internet
Off-the-shelf AI-image detectors are trained once, on generic web data, and never adapt to what's actually coming into your campaign. NewSpark instead builds a classifier from your own review history — every approve/reject decision your moderators make becomes a labeled training example specific to your content, your audience, and the generative tools currently being used against it.
🧬 Trained on real moderation decisions
No separate labeling project required — the training data is simply the real/fake calls your team already makes reviewing submissions.
📊 Transparent accuracy reporting
Every training run shows sample counts, the real/fake balance of your data, and measured accuracy — no black box, no vendor-supplied benchmark from someone else's dataset.
🔁 Retrain as new patterns emerge
Generative tools change constantly. Kick off a new training run whenever you want the model caught up on the latest submissions your moderators have reviewed.
🎯 Scoped per project
A model trained on one campaign's submissions doesn't have to guess at a different campaign's audience — each project can train its own.
Built into moderation, not bolted on after
Authenticity scoring shows up right where your team is already working — the same moderation queue used for rights tracking and approval workflows across NewSpark's UGC & gamification tools. There's no separate dashboard to check, and no submission is auto-rejected by the model alone — a human always makes the final call.
Questions we hear a lot
Does this replace human moderators?
No — it's a decision-support tool for your existing moderation queue. Every submission still goes through review; the model surfaces a likelihood score so your team can prioritize what to look at closely instead of treating every submission as equally trustworthy.
How is this different from a generic AI-image detector?
Generic detectors are trained once on the open web and never see your actual submissions. NewSpark trains a model per project, using your own moderators' real/fake decisions as labeled training data — so it learns the specific patterns of content coming into your specific campaign.
Can I see how accurate the model is?
Yes. Every training run reports accuracy against a held-out sample, and you can see the real/fake balance of your training data at a glance before deciding whether to retrain.
Do I need a data science team to use it?
No. Training happens from inside the same moderation workflow your team already uses — approve or reject a submission as usual, and that decision becomes a labeled example. Starting a new training run is a single click.
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