This thesis explores the application of Benford's Law to wavelet coefficients derived from the Discrete Wavelet Transform (DWT) of images, aiming to provide a novel method for image differentiation. The study focuses on the DWT, specifically utilizing Haar and Daubechies wavelets, to decompose an image into approximation and detail coefficients. Benford's Law, predicting the frequency distribution of leading digits in natural datasets, is applied to the detail coefficients. The research investigates whether this approach can differentiate natural images from other genres, such as paintings or cartoons. The thesis provides a comprehensive understanding of wavelets, the DWT, and signal decomposition, followed by an introduction to Benford's Law and its applications. The final part involves applying Benford’s Law to the DWT coefficients of different image genres, analyzing the results, and discussing further research.