min-entropy

The pytorch implementation of the Min-Entropy Latent Model for Weakly Supervised Object Detection

Min-Entropy Latent Model for Weakly Supervised Object Detection

Data and code to design and evaluate the PLL-based true random number generator according to the paper "Enhancing Quality and Security of the PLL-TRNG" (published and awarded at TCHES 2023).

Code repository for "Machine Learning Predictors for Min-Entropy Estimation" (arXiv:2406.19983). Implements RCNN and GPT-2 models for entropy prediction in RNGs, with data generation, training pipelines, and analysis scripts.

Field guide to randomness, entropy and RNGs: PRNG vs TRNG vs CSPRNG, the NIST SP 800-90A/B/C pipeline, min-entropy, conditioning, real-world seeding failures, and why Cloudflare's lava lamps harvest entropy instead of creating it. Born from a collaborative cross-review of a YouTube thread.

Formal verification of NIST SP 800-90B entropy estimators in Lean 4. Companion project to lopt's TRNG service; theorems intended for upstream contribution to mathlib.

Browser-based QRNG demo — NIST SP 800-90B. Min-entropy is what an optimal guesser faces; Shannon is the marketing number. Slide the detector bias and watch them diverge. Von Neumann fixes bias but not correlation — feed it a correlated stream and watch it output garbage while claiming success. Real Toeplitz extraction over GF(2). No backends.