Steganographic and watermarking information inserted into a color image file, regardless of embedding algorithm, causes disturbances in the relationships between neighboring pixels. A method for steganalysis utilizing the local binary pattern (LBP) texture operator to examine the pixel texture patterns within neighborhoods across the color planes is presented. Providing the outputs of this simple algorithm to an artificial neural net capable of supervised learning results in the creation of a surprisingly reliable predictor of steganographic content, even with relatively small amounts of embedded data. Other tools for identifying images with steganographic content have been developed by forming a neural network input vector comprised of image statistics that respond to particular side effects of specific embedding algorithms. The neural net in our experiment is trained with general texture related statistics from clean images and images modified using only one embedding algorithm, and is able to correctly discriminate clean images from images altered by data embedded by one of various different watermarking and steganographic algorithms. Algorithms tested include various steganographic and watermarking programs and include spatial and transform domain image hiding techniques. The interesting result is that clean color images can be reliably distinguished from steganographically-altered images based on texture alone, regardless of the embedding algorithm.