The Internet of Things (IoT) is cutting-edge technology of recent decade and has influenced all aspects of our modern life. Its significance and wide-range applications necessitate imposing security and forensics techniques on IoT to obtain more reliability. Digital cameras are the noteworthy part of IoT that play a vital role in the variety of usages and this entails proposing forensic solutions to protect IoT and mitigate misapplication.
Identifying source camera of an image is an imperative subject in digital forensics. Noise characteristics of image, extraction of Sensor Pattern Noise (SPN) and its correlation with Photo Response Non-Uniformity (PRNU) has been employed in the majority of previously proposed methods. In this paper, a feature extraction method based on PRNU is proposed which provides features for classification with Support Vector Machine (SVM). The proposed method endeavours to separate more powerful signals which is linked to camera sensor pattern noise by identifying color filter array pattern. To overcome the computational complexity, the proposed method is boosted by utilizing wavelet transform plus reducing dimensions of the image by selecting the most important components of noise. Our experiments demonstrate that the proposed method outperforms in terms of accuracy and runtime.