Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic algorithms and approaches to detect modifications done to an image. However, these detectors are model-driven (i.e., manipulation-specific) and the choice of a potent detector requires knowledge of the type of manipulation, something that cannot be known ( a priori ). Thus, the latest effort is directed towards developing model-free (i.e., generalized) detectors capable of detecting multiple manipulation types. In this article, we propose a novel detector capable of exposing seven different manipulation types in low-resolution compressed images. Our proposed approach is based on a two-layer convolutional neural network (CNN) to extract frequency domain features of image median filtered residual that are classified using two different classifiers—softmax and extremely randomized trees. Extensive experiments demonstrate the efficacy of proposed detector over existing state-of-the-art detectors.

Median filtering forensics in images has gained wide attention from researchers in recent years because of its inherent nature of preserving visual traces. Although many forensic methods are developed for median filtering detection, probability of detection reduces under JPEG compression at low-quality factors and for low-resolution images. The feature set reduction is also a challenging issue among existing detectors. In this article, a 19-dimensional feature set is analytically derived from image skewness and kurtosis histograms. This new feature set is exploited for the purpose of global median filtering forensics and verified with exhaustive experimental results. The efficacy of the method is tested on six popular databases (UCID, BOWS2, BOSSBase, NRCS, RAISE, and DID) and found that the new feature set uncovers filtering traces for moderate, low JPEG post-compression and low-resolution operation. Our proposed method yields lowest probability of error and largest area under the ROC curve for most of the test cases in comparison with previous approaches. Some novel test cases are introduced to thoroughly assess the benefits and limitations of the proposed method. The obtained results indicate that the proposed method would provide an important tool to the field of passive image forensics.

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Median filtering forensics in images is a subject under intense study nowadays. Existing median filtering detectors are developed based on hand‐crafted features and convolutional neural networks (CNN). Among hand‐crafted features based detectors, most of the detector's performance deteriorate for low‐resolution images compressed with low‐quality factors. However, CNN‐based detectors are found to be more robust at the expense of large database and large training time requirement. In this study, the authors propose a robust median filtering detector by exploiting the statistics of the Pearson parameter κ, κ is defined as the polynomial ratio of skewness and kurtosis. To capture fingerprints of median filtering, κ is determined for the median filtered residual (MFR) of the images to construct a novel feature set of 23 dimensions. The efficacy of the proposed feature set, against existing hand‐crafted features based and CNN‐based detectors, is established by a series of experiments for global median filtering detection. Results reveal that the proposed feature set exhibits performance gain of 2–4% against existing hand‐crafted features based detectors and an approximate gain of 4% against CNN‐based detector for detection of low‐resolution median filtered images compressed with low‐quality factors.