Multiscale Local Gabor Phase Quantization for image forgery detection

• Published in: Multimedia tools and applications 2017-12, Vol.76 (24), p.25851-25872
• Main Authors: Isaac, Meera Mary, Wilscy, M.
• Format: Article
• Language: English
• Subjects: Channels; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Feature extraction; Forgery; Image classification; Image detection; Image manipulation; Mathematical analysis; Matrix methods; Measurement; Morlet wavelet; Multimedia Information Systems; Researchers; Special Purpose and Application-Based Systems; Support vector machines; Texture
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-017-5189-5

Image Forgery is a field that has attracted the attention of a significant number of researchers in the recent years. The widespread popularity of imagery applications and the advent of powerful and inexpensive cameras are among the numerous reasons that have contributed to this upward spike in the reach of image manipulation. A considerable number of features – including numerous texture features – have been proposed by various researchers for identifying image forgery. However, detecting forgery in images utilizing texture-based features have not been explored to its full potential – especially a thorough evaluation of the texture features have not been proposed. In this paper, features based on image textures are extracted and combined in a specific way to detect the presence of image forgery. First, the input image is converted to YCbCr color space to extract the chroma channels. Gabor Wavelets and Local Phase Quantization are subsequently applied to these channels to extract the texture features at different scales and orientations. These features are then optimized using Non-negative Matrix Factorization (NMF) and fed to a Support Vector Machine (SVM) classifier. This method leads to the classification of images with accuracies of 99.33%, 96.3%, 97.6%, 85%, and 96.36% for the CASIA v2.0 , CASIA v1.0 , CUISDE , IFS-TC and Unisa TIDE datasets respectively showcasing its ability to identify image forgeries under varying conditions. With CASIA v2.0 , the detection accuracy outperforms the recent state-of-the-art methods, and with the other datasets, it gives a comparable performance with much reduced feature dimensions.