Nowadays, image forgery is a widespread problem in our lives. Image splicing is one method of forging images, and it has become a common tool for malicious users to modify images. Human eyes cannot perceive forgery operations on forged images; therefore, designing an expert system for detecting image authenticity has become a necessity. The splicing method causes some distortions that can be used to detect forgery on a tampered image. In this work, an expert system is designed that uses the statistical and textural properties of an image in a hybrid manner to detect forgery. The method extracts properties (statistical and textural) from high-level sub-bands of stationary wavelet transform (SWT) domain. SWT is used to obtain image's details in a multi scale manner in frequency domain. The method extracts statistical features from three sub-bands via Markov model, which generates transition probability matrices and then obtains Haralic's textural features based on gray level co-occurrence matrices. Features from each sub-bands are concatenated to form final feature vector. This expert system is the first in the literature to be designed using textural and statistical features together. Finally, the designed expert system classifies images as authentic or forged using SVM. The system's performance was evaluated using three publicly available image databases and tested for different attack types. The experimental results show that using statistical and textural features in a hybrid manner improves detection accuracy when compared to similar works in the literature. The main impact of the proposed system is its high accuracy and robust detection of different attack types. (C) 2019 Elsevier Ltd. All rights reserved.