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 graylevel 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

A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table

Personal Authentication Using Finger Images

As the need for personal authentication increases, biometrics systems have become the ideal answer to the security needs. This paper presents a novel personal authentication system which uses simultaneously acquired finger-vein and finger texture images of the same person. A virtual fingerprint is generated combining these two images. The result of the combination i.e. the virtual fingerprint is then subjected to pre-processing steps including binarization, normalization, enhancement and Region of Interest (ROI) segmentation. Gabor filter is used to extract features. The feature extracted image is matched with the database. This proposed system is designed such that to achieve better performance in terms of matching accuracy, execution time, memory required and security. DOI: 10.17762/ijritcc2321-8169.15017

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

A Survey of Iris Recognition System

The uniqueness of iris texture makes it one of the reliable physiological biometric traits compare to the other biometric traits. In this paper, we investigate a different level of fusion approach in iris image. Although, a number of iris recognition methods has been proposed in recent years, however most of them focus on the feature extraction and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power in the feature space, thus we conduct an analysis to investigate which fusion level is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows feature level fusion produce 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1

On the Reverse Engineering of the Citadel Botnet

Citadel is an advanced information-stealing malware which targets financial information. This malware poses a real threat against the confidentiality and integrity of personal and business data. A joint operation was recently conducted by the FBI and the Microsoft Digital Crimes Unit in order to take down Citadel command-and-control servers. The operation caused some disruption in the botnet but has not stopped it completely. Due to the complex structure and advanced anti-reverse engineering techniques, the Citadel malware analysis process is both challenging and time-consuming. This allows cyber criminals to carry on with their attacks while the analysis is still in progress. In this paper, we present the results of the Citadel reverse engineering and provide additional insight into the functionality, inner workings, and open source components of the malware. In order to accelerate the reverse engineering process, we propose a clone-based analysis methodology. Citadel is an offspring of a previously analyzed malware called Zeus; thus, using the former as a reference, we can measure and quantify the similarities and differences of the new variant. Two types of code analysis techniques are provided in the methodology, namely assembly to source code matching and binary clone detection. The methodology can help reduce the number of functions requiring manual analysis. The analysis results prove that the approach is promising in Citadel malware analysis. Furthermore, the same approach is applicable to similar malware analysis scenarios.Comment: 10 pages, 17 figures. This is an updated / edited version of a paper appeared in FPS 201