Detect Deepfakes vs. Hive — Honest Comparison
How Detect Deepfakes (powered by Resemble AI) compares to Hive Moderation on audio, image, and video deepfake detection — accuracy, speed, pricing model, and deployment options.
| Dimension | Detect Deepfakes | Hive |
|---|---|---|
| Primary strength | Zero-shot deepfake detection across audio, image, and video — same models that power Resemble AI enterprise | General-purpose content moderation platform (NSFW, AI-generated, violence) with broad class coverage |
| Audio detection | Dedicated zero-shot audio deepfake model, 98% accuracy, ~300ms latency | Audio moderation focused on language/content; AI-voice detection more recent addition |
| Image detection | Dedicated AI-image detector trained across diffusion, GAN, and autoencoder outputs | Strong AI-image classifier with multi-model coverage (SD, Midjourney, DALL·E, FLUX) |
| Video detection | Dual-track (visual + audio) analysis; fuses per-frame and temporal signals | Video moderation pipeline with AI-generated content detection as one signal |
| Pricing | Free on this site; pay-per-scan via Resemble AI API starting at $0.04/sec for audio | API pricing via custom quote; enterprise contracts typical |
| Best fit | Teams where deepfake-specific accuracy matters — banking, KYC, journalism, content verification | Platforms needing broad content moderation with deepfake as one of many signals |
Hive is a well-regarded content-moderation platform with deepfake detection as one of its many classifiers. Detect Deepfakes / Resemble AI is a dedicated deepfake-detection product. This page compares them honestly.
Where Hive is strong
Hive's content-moderation suite is broad — NSFW, violence, hate speech, AI-generated content, and more — delivered through a single API. For a platform that needs policy enforcement across many content categories, the one-vendor consolidation is a real advantage. Hive's AI-image classifier specifically is mature and supports many generator families.
Where Resemble AI is strong
Resemble AI's deepfake detection models are purpose-built single-task systems. The audio detector in particular is widely regarded as best-in-class on the zero-shot benchmarks — detecting voices synthesized by pipelines the detector hasn't seen during training. The video detector uses dual-track analysis (visual + audio) that catches lip-sync attacks where visual-only detection fails.
On most published deepfake-specific benchmarks, single-purpose models trained specifically on the synthesis-fingerprint detection task outperform multi-class moderation classifiers that treat deepfakes as one category among many.
How to choose
Pick Hive if:
- You need broad content moderation across many categories.
- Your deepfake detection needs are "good enough" rather than best-in-class.
- You want one vendor contract for all moderation needs.
Pick Resemble AI / Detect Deepfakes if:
- Deepfake-specific accuracy is the primary requirement.
- You need zero-shot generalization to synthesis methods the detector hasn't seen.
- You're in a high-stakes vertical — banking, insurance, KYC, journalism, elections — where detection quality directly drives business outcomes.
Pick both if:
- You're a large platform with both broad-moderation and deepfake-specific-verification needs.
Try both
Run a file through our free detector and through Hive's demo. Use the same file. See what each flags and how confident each is. For most teams that's the fastest honest comparison.