Visible to the public Morphological Filter Detector for Image Forensics Applications

TitleMorphological Filter Detector for Image Forensics Applications
Publication TypeJournal Article
Year of Publication2020
AuthorsBoato, G., Dang-Nguyen, D., Natale, F. G. B. De
JournalIEEE Access
Keywordsbinary image compression, binary level documents, data compression, Detectors, deterministic approach, digital image forensics, feature extraction, feature extractor, gray level documents, Gray-scale, grayscale image compression, Human Behavior, image classification, Image coding, image colour analysis, image filtering, image forensic identification, Image forensics, image manipulations, information forensics, Kernel, mathematical morphology, media authentication, Metrics, morphological filter detection, morphological filter detector, Morphology, noise removal, nonlinear image operators, pubcrawl, resilience, Resiliency, Scalability, Support vector machines, trained SVM classifier, Transform coding
AbstractMathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
Citation Keyboato_morphological_2020