This paper aims at detecting traces of median filtering in digital images, a problem of paramount importance in forensics given that filtering can be used to conceal traces of image tampering such as resampling and light direction in photomontages. To accomplish this objective, we present a novel approach based on multiple and multiscale progressive perturbations on images able to capture different median filtering traces through using image quality metrics. Such measures are then used to build a discriminative feature space suitable for proper classification regarding whether or not a given image contains signs of filtering. Experiments using a real-world scenario with compressed and uncompressed images show the effectiveness of the proposed method.