Portable True Random Number Generator Using Image Noise
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing random number generators, particularly pseudorandom number generators, face challenges such as bias, predictability, and limited cycle periods, while physical systems are complex and not portable, and previous methods using digitized images are cumbersome and dependent on specific environmental conditions.
Innovation Solution
A portable true random number generator that extracts microstructure noise and quantization errors from digital images using off-the-shelf devices like smartphones, processing the least significant bits of pixel data to generate a robust and unbiased random bit stream, which can be used directly or to seed pseudorandom number generators, and is applicable to both static and dynamic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pseudorandom number generators are used, then portability and ease of operation are improved, but reliability and randomness quality deteriorate due to bias and predictability
Solution Approach 1:
The patent extracts only the least significant bits (LSBs) from digitized image data, discarding the more significant bits that contain structured information. This extraction of the noisy, random-looking LSBs provides a portable method that maintains reliability by using the inherently random quantum noise present in digital sensor data.
Solution Approach 2:
The patent uses digitized copies of physical images (from photographs, videos, or other image sources) as the basis for random number generation. By copying visual information into digital form and then extracting LSB noise, the system achieves portability through software implementation while maintaining reliability through the physical origin of the random noise.
2Reliability
If physical random number generators are used, then reliability and randomness quality are improved, but device complexity and portability deteriorate
Solution Approach 1:
The patent replaces complex physical random number generator hardware (such as radioactive decay sources, electronic noise circuits, or thermal noise generators) with a software-based extraction method that uses existing digital image data. This substitution maintains reliability by still utilizing physical noise sources (quantum noise in sensors) while eliminating the need for specialized hardware, thereby improving portability.
Solution Approach 2:
The patent makes the random number generator universal by using digitized images from any source (photographs, videos, camera feeds, stored images) as input. This multi-functionality allows the same software algorithm to work across different devices and image sources, eliminating the need for device-specific hardware implementations and thereby improving portability while maintaining reliability.
3Productivity
If all bits of pixel data are used for random number generation, then productivity is improved, but reliability deteriorates due to interdependence and bias in higher bits
Solution Approach 1:
The patent extracts only the least significant bits (LSBs) from pixel data, deliberately excluding the more significant bits that contain correlated and biased information. This selective extraction improves reliability by using only the random noise present in LSBs, while still achieving productivity by processing entire images and extracting multiple LSBs per pixel.
Solution Approach 2:
The patent segments the pixel data by bit significance, separating the LSBs (which contain random noise) from the more significant bits (which contain structured information). This segmentation allows the system to use only the reliable random portion of the data while discarding the biased portions, thereby maintaining both reliability and productivity.
4Ease of operation
If previous image-based random number methods are used, then portability is improved, but reliability deteriorates due to dependence on environmental conditions
Solution Approach 1:
The patent changes the parameter being extracted from images from previously used methods (such as luminance variations or color transitions) to specifically targeting the least significant bits of digitized pixel values. This parameter change makes the system reliable by focusing on the quantum noise inherent in digital sensors, which is independent of environmental conditions like lighting or temperature, while maintaining portability through software implementation.
Data Source
AI summary
A method of providing a portable true random number generator based on the microstructure and noise found in digital images is claimed and disclosed. Using the lower significant bits of digitized images, strings of binary data are extracted. These raw strings are shown to pass the DIEHARD, NIST, and ENT tests for randomness for a robust selection of natural images. This information is available to, and may be processed by off-the-shelf technology including smartphones or other embedded devices without undue constraints on physical and environmental parameters. The method represents a significantly improved portable means of random number generation for all security, cryptographic, entertainment and PSI applications.