Mirage — Detect Image Editing
MRRdeals Score · 41/100What this SaaS is
Mirage uses machine learning to detect image edits and attempt to reverse them.
- Machine-learning image-edit detection
- Image-edit reversal
Numbers that matter
Mobile app
Apple and Google keep 15–30% of subscriptions: check whether the revenue and margin above are before or after the store commission, and plan the transfer of the app and its subscriptions through App Store Connect or Google Play.
Score breakdown
How the composite score above splits across seven independent signals. Each axis is graded 0-100. Hover for details.
Payback at the stated margin, asking multiple vs the category median, and profit margin. The heaviest axis: what you pay for what it earns.
Revenue trend over the last ~90 days from our daily snapshots (30-day figure when history is short), capped so one spike can't dominate, minus a penalty for erratic revenue.
Paying customers, revenue scale and traffic (log scales), plus live-site health (reachable pricing page, core pages, maintained site).
AI-disruption risk, organic search presence, recurring B2B revenue; penalties for thin AI wrappers and no-code lock-in.
How turnkey the takeover is: diversity of marketing channels, Merchant-of-Record status, team.
Higher = safer. Business age, breadth of the customer base (concentration risk), recurring vs one-off revenue, known payment provider.
Structured market read across TAM, saturation, 3-year trend, and hype cycle, blended with our category momentum signal.
Market read
- TAMmid
The app targets a broad consumer base, indicating a mid-sized market.
- Saturationgrowing
The market for image editing detection is growing as awareness of deepfakes increases.
- 3y trendgrowing
Consumer interest in authenticity and image verification has been rising.
- AI disruptionresilient
The technology is complex and not easily replicable by solo developers.
- Hype cycleearly
The concept is still emerging and not widely adopted yet.
Run the numbers
Before you make an offer
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