Journal ArticleDOI
Blind Forensics of Median Filtering in Digital Images
TLDR
This paper proposes a novel approach for detecting median filtering in digital images, which can accurately detect Median filtering in arbitrary images, even reliably detect median filters in low-resolution and JPEG compressed images; and reliably detect tampering when part of a Median filter is inserted into a nonmedian-filtered image, or vice versa.Abstract:
Exposing the processing history of a digital image is an important problem for forensic analyzers and steganalyzers. As the median filter is a popular nonlinear denoising operator, the blind forensics of median filtering is particularly interesting. This paper proposes a novel approach for detecting median filtering in digital images, which can 1) accurately detect median filtering in arbitrary images, even reliably detect median filtering in low-resolution and JPEG compressed images; and 2) reliably detect tampering when part of a median-filtered image is inserted into a nonmedian-filtered image, or vice versa. The effectiveness of the proposed approach is exhaustively evaluated in five different image databases.read more
Citations
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Journal ArticleDOI
An Overview on Image Forensics
TL;DR: The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition- based methods, coding-based methods, and editing-based schemes.
Journal ArticleDOI
Median Filtering Forensics Based on Convolutional Neural Networks
TL;DR: This work proposes a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image and achieves significant performance improvements, especially in the cut-and-paste forgery detection.
Journal ArticleDOI
Information Forensics: An Overview of the First Decade
TL;DR: An overview on what has been done over the last decade in the new and emerging field of information forensics regarding theories, methodologies, state-of-the-art techniques, major applications, and to provide an outlook of the future is provided.
Journal ArticleDOI
Robust Median Filtering Forensics Using an Autoregressive Model
TL;DR: A new, robust median filtering forensic technique that operates by analyzing the statistical properties of the median filter residual (MFR), which is defined as the difference between an image in question and a median filtered version of itself.
Book ChapterDOI
Counter-Forensics: Attacking Image Forensics
Rainer Böhme,Matthias Kirchner +1 more
TL;DR: This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images, and develops terminology that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks.
References
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A Practical Guide to Support Vector Classication
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
Proceedings ArticleDOI
UCID: an uncompressed color image database
Gerald Schaefer,Michal Stich +1 more
TL;DR: A new dataset, UCID (pronounced "use it") - an Uncompressed Colour Image Dataset which tries to bridge the gap between standardised image databases and objective evaluation of image retrieval algorithms that operate in the compressed domain.
Journal ArticleDOI
Steganalysis by Subtractive Pixel Adjacency Matrix
TL;DR: A method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching.
Book ChapterDOI
Using high-dimensional image models to perform highly undetectable steganography
TL;DR: A complete methodology for designing practical and highly-undetectable stegosystems for real digital media and explains why high-dimensional models might be problem in steganalysis, and introduces HUGO, a new embedding algorithm for spatial-domain digital images and its performance with LSB matching.
Journal ArticleDOI
Steganalysis of LSB matching in grayscale images
TL;DR: Two novel ways of applying the histogram characteristic function (HCF), introduced by Harmsen for the detection of steganography in color images but ineffective on grayscale images, are introduced: calibrating the output using a downsampled image and computing the adjacency histogram instead of the usual histogram.