No abstract

This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position in the same image. The proposed approach for splicing detection is based on the assumption that illumination between the original and tampered images is different. To detect the difference between the original and tampered images, the homomorphic transform separates the illumination component from the reflectance component. The illumination histogram derivative is used for detecting the difference in illumination, and hence forgery detection is accomplished. Prior to performing the forgery detection process, some pre-processing techniques, including histogram equalization, histogram matching, high-pass filtering, homomorphic enhancement, and single image super-resolution, are introduced to reinforce the details and changes between the original and embedded sections. The proposed approach for copy-move forgery detection is performed with the Speeded Up Robust Features (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matching with Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogram equalization and single-mage super-resolution. Simulation results proved the feasibility and the robustness of the pre-processing step in homomorphic detection and SURF detection algorithms for splicing and copy-move forgery detection, respectively.

The headway of modern technology and facility to use processing software leads to tamper and implicate of digital images. This tampering is being performed without leaving any a clear effect noted with the naked eye. The discrimination between different authentic and forged images can be based on its Probability Density Functions (PDFs). This paper introduces a new model for digital image forgery detection. This framework has two main phases; training and testing. In the training phase, the peak is calculated for the derivatives histogram of the illumination components by using homomorphic filter to separate the illumination components on each image. Firstly, the derivative of illumination histogram for authentic and forged images is calculated then the PDFs are estimated for authentic and forged images, finally the threshold is determined. In the testing phase, the determined threshold is tested with realistic dataset followed by using the selected bins for feature calculation in the prediction process. In the final prediction step, a detection and decision process is performed to obtain performance of the new model. This new model is provided a very effective performance. Different color image contrast systems RGB and HIS are studied and utilized for testing our model and compare between each channel for two systems to estimate performance and obtain more sensitive channel.