Feature-based steganalysis has been an integral tool for detecting the presence of steganography in communication channels for a long time. In this paper, we explore the possibility to utilize powerful optimization algorithms available in convolutional neural network packages to optimize the design of rich features. To this end, we implemented a new layer that simulates the formation of histograms from truncated and quantized noise residuals computed by convolution. Our goal is to show the potential to compactify and further optimize existing features, such as the projection spatial rich model (PSRM).
It is widely recognized that steganography with sideinformation in the form of a precover at the sender enjoys significantly higher empirical security than other embedding schemes. Despite the success of side-informed steganography, current designs are purely heuristic and little has been done to develop the embedding rule from first principles. Building upon the recently proposed MiPOD steganography, in this paper we impose multivariate Gaussian model on acquisition noise and estimate its parameters from the available precover. The embedding is then designed to minimize the KL divergence between cover and stego distributions. In contrast to existing heuristic algorithms that modulate the embedding costs by 1–2| e |, where e is the rounding error, in our model-based approach the sender should modulate the steganographic Fisher information, which is a loose equivalent of embedding costs, by (1–2| e |)^2. Experiments with uncompressed and JPEG images show promise of this theoretically well-founded approach.
With the rapid development of mobile devices and multimedia processing technologies, digital multimedia applications has become increasingly more popular in our daily life. Due to the nature of digital media, digital images can be easily modified without leaving obvious traces. Digital image forensics is an emerging research field which aims to address the major problems as forgery detection, source identification, image recovery and detecting the existence of hidden information, which is also referred as steganalysis. Steganography is the science of covert communication which aims to conceal the existence of the secret information hidden in the communication. Steganalysis is the study of detecting hidden information, which is embedded by using steganography techniques. In this paper we present a forensic mobile application developed on iOS platform. This application is designed to perform both steganalysis and steganorgraphy tasks in digital images; that is to conduct information analysis of given images and determine if there is any secret information hidden in the given images, and also fulfill the task of hiding information invisibly into digital images. There have been a number of well-established techniques in digital image steganalysis and steganography fields during recent years. However, there are very few mobile forensic tools that have been developed to comprehensively adopt these methods. Our forensic mobile application aims to systematically include significant methods both in steganography and steganalysis fields, so that people can use it as a forensic tool to determine if an image contains a hidden message, as well as use it as a security tool to perform covert communication by hiding information in an image.