Digital image splicing detection based on Markov features in DCT and DWT domain

Accurate unsupervised tampering localization is one of the most challenging problems in digital image forensics. In this paper, we consider a photo response non-uniformity analysis and focus on the detection of small forgeries. For this purpose, we adopt a recently proposed paradigm of multi-scale analysis and discuss various strategies for its implementation. First, we consider a multi-scale fusion approach, which involves combination of multiple candidate tampering probability maps into a single, more reliable decision map. The candidate maps are obtained with sliding windows of various sizes and thus allow to exploit the benefits of both the small- and large-scale analyses. We extend this approach by introducing modulated threshold drift and content-dependent neighborhood interactions, leading to improved localization performance with superior shape representation and easier detection of small forgeries. We also discuss two novel alternative strategies: a segmentation-guided approach, which contracts the decision statistic to a central segment within each analysis window and an adaptive-window approach, which dynamically chooses analysis window size for each location in the image. We perform extensive experimental evaluation on both synthetic and realistic forgeries and discuss in detail practical aspects of parameter selection. Our evaluation shows that the multi-scale analysis leads to significant performance improvement compared with the commonly used single-scale approach. The proposed multi-scale fusion strategy delivers stable results with consistent improvement in various test scenarios.

Multi-Scale Analysis Strategies in PRNU-Based Tampering Localization

Digital image integrity – a survey of protection and verification techniques

Multi-scale noise estimation for image splicing forgery detection

In this age of digitization, digital images are used as a prominent carrier of visual information. Images are becoming increasingly ubiquitous in everyday life. Unprecedented involvement of digital images can be seen in various paramount fields like medical science, journalism, sports, criminal investigation, image forensic, etc., where authenticity of image is of vital importance. Various tools are available free of cost or with a negligible amount of cost for manipulating images. Some tools can manipulate images to such an extent that it becomes impossible to discriminate by human visual system that image is forged or genuine. Hence, image forgery detection is a challenging area of research. It is evident that good quality work has been carried out in the past decade in the field of image forgery detection. However, there is still a need to pay much attention in this field, as image manipulation tools are becoming more and more sophisticated. The main purpose of this paper is to review the various existing methods developed for detecting the image forgery. A categorization of various forgery detection techniques has been presented in the paper.

Image Forgery Detection: Survey and Future Directions

The extensive availability of sophisticated image editing tools has rendered it relatively easy to produce fake images. Image splicing is a form of tampering in which an original image is altered by copying a portion from a different source. Because the phenomenon of motion blur is a common occurrence in hand-held cameras, we propose a passive method to automatically detect image splicing using blur as a cue. Specifically, we address the scenario of a static scene in which the cause of blur is due to hand shake. Existing methods for dealing with this problem work only in the presence of uniform space-invariant blur. In contrast, our method can expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations. We validate our method on several examples for different scene situations and camera motions of interest.

Makkena Purnachandra Rao

Papers

Harnessing Motion Blur to Unveil Splicing