Morphological Filter Detector for Image Forensics Applications

Mathematical 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, litt...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.13549-13560
Main Authors: Boato, Giulia, Dang-Nguyen, Duc-Tien, De Natale, Francesco G. B.
Format: Article
Language:English
Subjects:
Accuracy
Datasets
Detectors
Digital Image Forensics
Feature extraction
Gray-scale
Image coding
Image compression
Image enhancement
Image filters
Image forensics
Image manipulation
Kernel
Mathematical morphology
media authentication
morphological filter detection
Morphological filters
Morphology
Operators (mathematics)
Q factors
Robustness (mathematics)
Support vector machines
Transform coding
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Summary:Mathematical 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2965745