Application of Benfordʼs Law to Images

Benford's Law ◽  
2015 ◽  
Author(s):  
Fernando Pérez-González ◽  
Tu-Thach Quach ◽  
Chaouki T. Abdallah ◽  
Gregory L. Heileman ◽  
Steven J. Miller
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Digital Camera ◽  
Benford’S Law ◽  
Natural Phenomena ◽  
Image Capture ◽  
Image Forensic ◽  
Benford's Law

This chapter analyzes the application of Benford's law to pictures taken from nature with a digital camera. Considering that many natural phenomena seem to follow Benford's law and that images are often nothing but “snapshots of nature,” it is pertinent to wonder whether images (at least those taken from nature) obey Benford's law. While the values output by the image capture device embedded in the camera, i.e., the pixels, do not follow Benford's law, this chapter shows that if they are transformed into a domain that better approximates the human visual system then the resulting values satisfy a generalized form of Benford's law. This can be used for image forensic applications, such as detecting whether an image has been modified to carry a hidden message (steganography) or has been compressed with some loss of quality.

Benford's law in medicinal chemistry: Implications for drug design

2019 ◽  
Vol 11 (17) ◽  
pp. 2247-2253 ◽  
Author(s):  
Alfonso T García-Sosa
Keyword(s):  
Machine Learning ◽  
Medicinal Chemistry ◽  
Pattern Mining ◽  
Benford’S Law ◽  
Log P ◽  
Data Generation ◽  
Natural Phenomena ◽  
Quality Of Data ◽  
Benford's Law ◽  
Predicted Values

Aim: The explosion of data based technology has accelerated pattern mining. However, it is clear that quality and bias of data impacts all machine learning and modeling. Results & methodology: A technique is presented for using the distribution of first significant digits of medicinal chemistry features: log P, log S, and p Ka. experimental and predicted, to assess their following of Benford's law as seen in many natural phenomena. Conclusion: Quality of data depends on the dataset sizes, diversity, and magnitudes. Profiling based on drugs may be too small or narrow; using larger sets of experimentally determined or predicted values recovers the distribution seen in other natural phenomena. This technique may be used to improve profiling, machine learning, large dataset assessment and other data based methods for better (automated) data generation and designing compounds.

Applications of Benford's Law

2015 ◽  
Author(s):  
Arno Berger ◽  
Theodore P. Hill
Keyword(s):  
Computer Science ◽  
Cross Section ◽  
Ad Hoc ◽  
Fraud Detection ◽  
Benford’S Law ◽  
Natural Phenomena ◽  
Pedagogical Tool ◽  
Practical Applications ◽  
Benford's Law ◽  
Main Ideas

This chapter provides a overview of the practical applications of Benford's law. These include fraud detection, detection of natural phenomena, diagnostics and design, computations and computer science, and as a pedagogical tool. In contrast to the rest of the book, this chapter is necessarily expository and informal. It has been organized into a handful of ad hoc categories, which the authors hope will help illuminate the main ideas. None of the conclusions of the experiments or data presented here have been scrutinized or verified by the authors of this book, since the intent here is not to promote or critique any specific application. Rather the goal is to offer a representative cross-section of the related scientific literature, in the hopes that this might continue to facilitate research in both the theory and practical applications of Benford's law.

Sign in / Sign up

Export Citation Format

Share Document