Morphing is a well-researched topic in computer graphics and image processing. Unlike cross-fading, morphing transforms a source image into a target image using distortions and the adjustment of predefined features (control points). This transition is intended to create results that are as realistic as possible, and it happens notably when the face on the source and target images does not differ too much. Therefore, the typical morphing process consists of warping essential image elements (e.g., facial features such as eyes, mouth, and facial contours) in the source and target image with the help of selected control points in such a way that these areas can be brought into conformity with each other or align themselves. For effects as close to reality as possible, the source and target images must not differ too much. The training course developed for this work is intended to introduce the dangers posed by the malicious manipulation of facial photographs, the so-called morphing attack. An algorithm matches the images of two different people so that the resulting facial image combines the identification features of both people. Research has shown that these images are difficult for the human eye to distinguish from real, unaltered photographs. The face morphing attack exploits the weakness in the application for identification. If a morphed passport photo remains undiscovered, a genuine identity document with a manipulated photo is issued that may allow two different persons to cross a border without authorization. This targeted training course consists of 10 modules for a one-week training course and is designed to help identify morphed facial images and reduce their acceptance at ID and passport control.
In recent years, ID controllers have observed an increase in the use of fraudulently obtained ID documents [1]. This often involves deception during the application process to get a genuine document with a manipulated passport photo. One of the methods used by fraudsters is the presentation of a morphed facial image. Face morphing is used to assign multiple identities to a biometric passport photo. It is possible to modify the photo so that two or more persons, usually the known applicant and one or more unknown companions, can use the passport to pass through a border control [2]. In this way, persons prohibited from crossing a border can cross it unnoticed using a face morphing attack and thus acquire a different identity. The face morphing attack aims to weaken the application for an identity card and issue a genuine identity document with a morphed facial image. A survey among experts at the Security Printers Conference revealed that a relevant number of at least 1,000 passports with morphed facial images had been detected in the last five years in Germany alone [1]. Furthermore, there are indications of a high number of unreported cases. This high presumed number of unreported cases can also be explained by the lack of morphed photographs’ detection capabilities. Such identity cards would be recognized if the controllers could recognize the morphed facial images. Various studies have shown that the human eye has a minimal ability to recognize morphed faces as such [2], [3], [4], [5], [6]. This work consists of two parts. Both parts are based on the complete development of a training course for passport control officers to detect morphed facial images. Part one contains the conception and the first test trials of how the training course has to be structured to achieve the desired goals and thus improve the detection of morphed facial images for passport inspectors. The second part of this thesis will include the complete training course and the evaluation of its effectiveness.
Nowadays, digital images are used as critical evidence for judgment, but they can be forged using image processing tools with invisible traces and little effort. Hence, it is very important to determine the authenticity of these digital images. In this paper, we propose a novel approach that uses dictionary learning and sparse coding to detect digital image forgery. We experimented with two popular data sets to determine how effectively and efficiently our approach detects digital image forgery compared to previous approaches. The results show that our approach not only outperforms these approaches in terms of Precision, Recall, and F1 score, but it is also more robust against compression and rotation attacks. Also, our approach detects forgery significantly faster than previous approaches since it uses a sparse representation that dramatically reduces the feature dimensionality by a factor of more than 20.
The reliability of many digital forensic techniques can be negatively affected by geometrical transforms applied to the image under investigation because it breaks pixel-topixel synchronization needed for example for forensic methods that rely on sensor fingerprints. The geometrical transform typically needs to be detected and then its parameters estimated to allow subsequent successful and reliable forensic analysis. This paper focuses on blind detection of rotation and estimation of the angle by which the image was rotated. The proposed method utilizes the so-called Linear Pattern (LP) as a global template. In particular, no side information, such as watermark or the EXIF header, is required. The method is generally applicable whenever the image under investigation had a sufficiently strong LP before rotation. The performance of the method is assessed experimentally and by comparing to previous art. The main advantage of the proposed method is its accuracy for estimating small rotation angles (less than 3 degrees). It will also work after resizing.