Spatial domain quantization noise based image filtering detection

Smart image editing and processing techniques make it easier to manipulate an image convincingly and also hide any artifacts of tampering. Common real world forgeries can be accompanied by enhancement operations like filtering, compression and/or format conversion to suppress forgery artifacts. Out of these enhancement operations, filtering is very common and has received a lot of attention in forensics research lately. However, different filtering operations and image formats are not investigated deeply and simultaneously. We propose an algorithm to detect if a given image has undergone filtering based enhancement irrespective of the format of image or the type of filter applied. In the proposed algorithm, we exploit the correlation of spatial domain quantization noise of an image by extracting transition probability features and classify the image as filtered or unfiltered. Experiments are performed to evaluate the robustness and compare the performance of the proposed technique with popular forensic filtering detection algorithms and is found to be superior in most of the cases.