IEEE 2017-2018 Information Forensics & Security Projects in Java
Abstract:Twitter trends, a timely updated set of top terms in Twitter, have the ability to affect the public agenda of the community and have attracted much attention. Unfortunately, in the wrong hands, Twitter trends can also be abused to mislead people. In this paper, we attempt to investigate whether Twitter trends are secure from the manipulation of malicious users. We collect more than 69 million tweets from 5 million accounts. Using the collected tweets, we first conduct a data analysis and discover evidence of Twitter trend manipulation. Then, we study at the topic level and infer the key factors that can determine whether a topic starts trending due to its popularity, coverage, transmission, potential coverage, or reputation. What we find is that except for transmission, all of factors above are closely related to trending. Finally, we further investigate the trending manipulation from the perspective of compromised and fake accounts and discuss countermeasures.
Abstract:Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.
Abstract:Key exposure is one serious security problem for cloud storage auditing. In order to deal with this problem, cloud storage auditing scheme with key-exposure resilience has been proposed. However, in such a scheme, the malicious cloud might still forge valid authenticators later than the key-exposure time period if it obtains the current secret key of data owner. In this paper, we innovatively propose a paradigm named strong key-exposure resilient auditing for secure cloud storage, in which the security of cloud storage auditing not only earlier than but also later than the key exposure can be preserved. We formalize the definition and the security model of this new kind of cloud storage auditing and design a concrete scheme. In our proposed scheme, the key exposure in one time period doesn’t affect the security of cloud storage auditing in other time periods. The rigorous security proof and the experimental results demonstrate that our proposed scheme achieves desirable security and efficiency.
Abstract:Image forgery is becoming a growing threat to information credibility. Among all kinds of image forgeries, photographic composites of human faces have very serious impacts. To combat this kind of forgery, some forensic methods propose to estimate the 3D lighting environments from different faces and investigate the consistency between them. Although they are very effective, existing 3D lighting-based forensic methods are limited by many simplifying assumptions about the surface reflection model, among which convexity and constant reflectance are two critical ones. In this paper, we propose an optimized 3D lighting estimation method by incorporating a more general surface reflection model. In this model, we relax the convexity and constant reflectance assumptions by taking the occlusion geometry and surface texture information into consideration. The proposed reflection model is more general and accurate; hence, it can achieve better lighting estimation accuracy and more reliable discrimination performance. Comprehensive experiments on both synthetic and real data sets validate the correctness and efficacy of the proposed method. Comparisons with two existing 3D lighting-based forensic methods also demonstrate the superiority of the proposed method for detecting face splicing.