
AI Detector Robustness: Data and Methods from Our RAID Reanalysis
Full aggregate tables from Violet's RAID reanalysis: detector accuracy under 11 attacks, per-document flip rates, per-domain false positives, methods.
VioletResearch
Research and operating notes on ecosystem growth, go-to-market execution, and growth engineering for crypto/Web3, AI and developer tools, and consumer fintech.

Full aggregate tables from Violet's RAID reanalysis: detector accuracy under 11 attacks, per-document flip rates, per-domain false positives, methods.

Measured on 480,000 generations: paraphrasing makes one detector 5.4pp more accurate and another 17.0pp worse. Evasion is a detector lottery, not a strategy.

One pooled 5% setting, eight writing domains of truth: measured per-domain detector false-positive rates range 0.96% to 20.4%.

Measured on 480,000 generations: one symbol substitution flips 93.7% of a detector's per-document verdicts. Aggregate accuracy hides the collapse.

A decision-first framework for choosing growth metrics without mistaking publishing, rankings, or TVL for attention, adoption, or customer value.

A research-backed framework for choosing creator capabilities, building a complementary portfolio, and testing each role against a defined growth outcome.

A practical operating system for translating meaningful product changes into clear explanations, useful distribution, measurable action, and market learning.

A practical system for deciding which public surfaces a consumer-crypto product should maintain, repair, consolidate, or deliberately decline.

Why synonym shuffling cannot guarantee clean provenance—plus RAID measurements of 11 attacks collapsing AI detectors, and Violet's integrity protocol.
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