

Research Interest: multimedia forensics, information hiding, image processing and pattern recognition
Projects 
# the National Natural Science Foundation of China (61702332)  Noise inconsistency based image forensic research using highorder statistical analysis and local homogeneity constraints 

Year 2018  2020 

Abstract: Image forensics is a frontier research topic in the field of information security. This project uses the highorder statisticalbased noise estimation method as the entry point to study the new theories and methods of noise estimation and its forensic applications. The detailed works are as follows. We will analyze the mechanism of the scaleinvariant kurtosis property, and investigate the influence of the differences in terms of the content and regular texture on stability of the kurtosis invariance. We will use the deep learning theory to extract the stable region with the best scaleinvariant kurtosis property, and construct a multiconstrained cost function to determine the global variance of the image. We will conduct the box filter technique to extent the global estimation method to pixellevel local method. After constructing a weighted mechanism for the confidence level of samples for estimation based on the local noise estimation, we will explore the new method to estimate the noise level function under the condition of uneven distribution of samples. After deriving the relationship model between the noise level function, the camera response function and the external adjusted curve, we will estimate the external curve based on the Bayesian theory. We will combine the features of spectral residual and the guided filtering error to measure the homogeneity of each pixel, and explore the monotone relation between the homogeneity and the noise estimation error. We will further propose an optimized samples selection and modification approach based on a stepwise updating strategy, and then estimate the maximum likelihood noise distribution for an image and the probability of each pixel obeying the distribution using the theories of sparse representation and maximum likelihood. The results of the project are of great significance to the theories and techniques of digital forensics and multimedia information security. 
# Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (MIMS1603)  A research on image forgery detections using camara intrinsic fingerprints 

Year 2017  2018 

Abstract: Digital image forensics is a hot and challenging topic for multimedia security researches, which is defined as a technique to identify image forgery without any prior knowledge or preembedded watermark. In this project, we develop new forensic approaches based on the analysis of camera imaging theory and integrated with the knowledge of applied optics, computer vision and statistics. In the noise inconsistency forgery detecting, we extract some regions of interest in one image and estimate their noise level. Once the noise level of any region is distinctly different with others, this region is treated as one suspected forgery region. Here, noise level of one region is a function of noise variance with respect to image intensity and can be estimated based on Bayesian maximum a posteriori
framework. An adaptive parameter is considered for the sake of texture
interference. For an image with wideangle (or telephoto) lens distortion, we
extract the straightline edges and estimate the principle point and lens
distortion parameters. After restoring the whole image based on the estimated
parameters, then we seek the objects in the restored image, whose edges are still
not yet linearized. Lens blur appears when using a relative high aperture of a
camera. We evaluate the blur radius of any object in the scene and analyze the
constraints between the following aspects: the blur level, the object height in
real scene and image and distance between the object to focus plane. Based on this
constraints, we design a forensic method to detect this kind of perspective
constraints based forgeries. Meanwhile, the intrinsic fingerprints left in the
process of imaging, such as camera filter array traces, can also be applied to
improve the efficiency of other information hiding techniques. 




 
