CAPTCHA Resilience & AI Model Solver Benchmark

Adversarial Optical Challenge

ROUND 1 OF 3

TYPE: OPTICAL_DISTORTION 280 × 100 PX

Adversarial Perturbation Vectors AI Weakness: HIGH

Noise Density 0.45

Glyph Rotation 28°

Higher noise and non-affine rotational distortion break CNN kernel feature maps and Transformer attention pooling while the human visual cortex performs gestalt closure.

Comparative AI vs Human Telemetry

REAL-TIME AUDIT

AI Error Rate

33.3%

Multimodal vision misclassifications

Human Avg Solve

1420 ms

Cognitive recognition latency

Distortion Resilience

88 / 100

Human vs AI perceptual edge

Benchmark Status

human_victory

Current aggregate outcome

AI VISION MODEL SIMULATION (GPT-4o / Claude Vision Class) AI CONFIDENCE: 61%

Target Glyph Sequence: K7X9M

Simulated AI Inference: K7XOM

Identified Failure Mode: Curve Confusion (9 → O)

Human Solve Reaction: 1420 ms (Match)

Benchmark Trial Audit Log 3 Trials Recorded

#	Type	Target	Human Time	AI Prediction	AI Result

Why LLMs Fail Text CAPTCHAs

Multimodal vision encoders break images into discrete patch tokens (e.g. 14×14 or 16×16 px). High-frequency salt-and-pepper noise and non-linear spline warps disrupt edge-continuity across adjacent patches, triggering hallucinations where '9' turns into 'O' or '8'.

Biological Gestalt Advantage

The primate visual cortex leverages horizontal recurrent connections in V1/V2 for contour completion and figure-ground segregation. Even when 45% of pixels are occluded by speckles or cross-lines, human perception filters noise instantly under 1500 ms.

Adversarial Vulnerability

Our benchmark tests the exact sensitivity boundary. Above 0.40 noise density and 25° rotation, simulated model accuracy falls precipitously, demonstrating why frontier models capable of passing coding exams still fail reverse-Turing tests.