Abstract
Whilst it is sometimes essential that a scene is well lit before image capture, too much light can cause exposure or glare-based problems. Typically, glare is introduced to images when the camera is pointed towards the light source, and results in a visible distortion in the image. In this paper, we analyse and identify images that contain the `glare' property using the empirical Benford's Law. The experiment is performed on 1338 images, and extracts discrete wavelet High High (HH), High Low (HL) and Low High (LH) sub bands as raw data. The significant digit from each coefficient of all sub bands is then calculated. We then analyse the probability of occurrence of large digits against smaller digits to detect anomalies. All images containing these anomalies are further analysed for the identification of additional salient features. This analysis is performed in accordance with the Benford's Law plot and the help of probability intensity histogram and divergence. Our results indicate that 142 images have irregular Benford's Law curves. For most images, the irregularity occurs at the $5^{th}$ digit. After visual examination, we have found the unbalanced light and high level of brightness in these images. To measure the intensity of light in an image, we compute the probability histogram of gray levels. These results also correlate with the irregular peak identified from the Benford's Law curves. In addition, the divergence is then computed, which shows the deviation between the actual Benford's Law curve and the Benford's Law graph of an image. Our proposed technique is novel and has a potential to be an image forensic tool for quick image analysis.