Fiducial markers play a crucial role in localization, navigation, and tracking systems, particularly in robotics. However, widely used marker detection methods relying on RGB cameras often fail in low-light or no-light environments, where traditional black-and-white markers become ineffective. To address this challenge, we propose a Variable Lighting Conditions Fiducial Marker (VLCFM) system that enhances detection accuracy across diverse lighting conditions, ensuring reliable performance in complete darkness while remaining undetectable to the human eye.

This paper presents a novel convolutional neural network (CNN) architecture for image steganalysis. We trained the CNN to detect distortions introduced by the state-of-the-art embedding algorithms: WOW, HUGO, and UNIWARD. We used a large database of test images and a large number of stegomessages, which resulted in a six billion-sample training set. The experimental results show that the resultant CNN, which was trained on such a large dataset, outperforms state-of-the-art steganalysis algorithms in terms of total error rate.