Biblio

We are currently witnessing the development of increasingly effective author identification systems (AISs) that have the potential to track users across the internet based on their writing style. In this paper, we discuss two methods for providing user anonymity with respect to writing style: Adversarial Stylometry and Adversarial Authorship. With Adversarial Stylometry, a user attempts to obfuscate their writing style by consciously altering it. With Adversarial Authorship, a user can select an author cluster target (ACT) and write toward this target with the intention of subverting an AIS so that the user's writing sample will be misclassified Our results show that Adversarial Authorship via interactive evolutionary hill-climbing outperforms Adversarial Stylometry.

Data Deduplication provides lots of benefits to security and privacy issues which can arise as user's sensitive data at risk of within and out of doors attacks. Traditional secret writing that provides knowledge confidentiality is incompatible with knowledge deduplication. Ancient secret writing wants completely different users to encode their knowledge with their own keys. Thus, identical knowledge copies of completely different various users can result in different ciphertexts that makes Deduplication not possible. Convergent secret writing has been planned to enforce knowledge confidentiality whereas creating Deduplication possible. It encrypts/decrypts a knowledge copy with a confluent key, that is obtained by computing the cryptographical hash price of the content of the information copy. Once generation of key and encryption, the user can retain the keys and send ciphertext to cloud.

Keystroke Dynamics can be used as an unobtrusive method to enhance password authentication, by checking the typing rhythm of the user. Fixed passwords will give an attacker the possibility to try to learn to mimic the typing behaviour of a victim. In this paper we will investigate the performance of a keystroke dynamic (KD) system when the users have to type given (English) words. Under the assumption that it is easy to type words in your native language and difficult in a foreign language will we also test the performance of such a challenge-based KD system when the challenges are not common English words, but words in the native language of the user. We collected data from participants with 6 different native language backgrounds and had them type random 8-12 character words in each of the 6 languages. The participants also typed random English words and random French words. English was assumed to be a language familiar to all participants, while French was not a native language to any participant and most likely most participants were not fluent in French. Analysis showed that using language dependent words gave a better performance of the challenge-based KD compared to an all English challenge-based system. When using words in a native language, then the performance of the participants with their mother-tongue equal to that native language had a similar performance compared to the all English challenge-based system, but the non-native speakers had an FMR that was significantly lower than the native language speakers. We found that native Telugu speakers had an FMR of less than 1% when writing Spanish or Slovak words. We also found that duration features were best to recognize genuine users, but latency features performed best to recognize non-native impostor users.

Arabic handwritten documents present specific challenges due to the cursive nature of the writing and the presence of diacritical marks. Moreover, one of the largest labeled database of Arabic handwritten documents, the OpenHart-NIST database includes specific noise, namely guidelines, that has to be addressed. We propose several approaches to process these documents. First a guideline detection approach has been developed, based on K-means, that detects the documents that include guidelines. We then propose a series of preprocessing at text-line level to reduce the noise effects. For text-lines including guidelines, a guideline removal preprocessing is described and existing keystroke restoration approaches are assessed. In addition, we propose a preprocessing that combines noise removal and deskewing by removing line fragments from neighboring text lines, while searching for the principal orientation of the text-line. We provide recognition results, showing the significant improvement brought by the proposed processings.

As mobile devices increasingly become bigger in terms of display and reliable in delivering paid entertainment and video content, we also see a rise in the presence of mobile applications that attempt to profit by streaming pirated content to unsuspected end-users. These applications are both paid and free and in the case of free applications, the source of funding appears to be advertisements that are displayed while the content is streamed to the device. In this paper, we assess the extent of content copyright infringement for mobile markets that span multiple platforms (iOS, Android, and Windows Mobile) and cover both official and unofficial mobile markets located across the world. Using a set of search keywords that point to titles of paid streaming content, we discovered 8,592 Android, 5,550 iOS, and 3,910 Windows mobile applications that matched our search criteria. Out of those applications, hundreds had links to either locally or remotely stored pirated content and were not developed, endorsed, or, in many cases, known to the owners of the copyrighted contents. We also revealed the network locations of 856,717 Uniform Resource Locators (URLs) pointing to back-end servers and cyber-lockers used to communicate the pirated content to the mobile application.

This paper presents a framework to identify the authors of Thai online messages. The identification is based on 53 writing attributes and the selected algorithms are support vector machine (SVM) and C4.5 decision tree. Experimental results indicate that the overall accuracies achieved by the SVM and the C4.5 were 79% and 75%, respectively. This difference was not statistically significant (at 95% confidence interval). As for the performance of identifying individual authors, in some cases the SVM was clearly better than the C4.5. But there were also other cases where both of them could not distinguish one author from another.