Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2],[4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.

This paper presents an adaptive least-significant-bit (LSB) steganography for spatial color images on smartphones. For each red, green, and blue color component, the combinations of All-4bit, One-4bit+Two-2bit, and Two-3bit+One-2bit LSB replacements are proposed for content-adaptivity and natural histograms. The high capacity of 8.4bpp with the average peak signal noise ratio (PSNR) 43.7db and fast processing times on smartphones are also demonstrated

A survey was conducted on the use of ICT by visually impaired people. Among 304 respondents, 81 used smartphones and 44, tablets. Blind people used feature phones at a higher rate and smartphones and tablets at lower rates than people with low vision. The most popular smartphone model was iPhone and the most popular tablet model was iPad. While almost all blind users used the speech output accessibility feature and only a few of them used visual features, low vision users used both visual features such as Zoom, Large text, and Invert colors and speech output at high rates both on smartphones and tablets. The most popular text entry methods were different between smartphones and tablets. For smartphones flick and numeric keypad input were popular among low vision users while voice input was the most popular among blind users. For tablets a software QWERTY keyboard was the most popular among both blind and low vision users. The advantages of smartphones were access to geographical information, quick Web browsing, voice input, and extensibility for both blind and low vision users, object recognition for blind users, and readability for low vision users. Tablets also work as a vision aid for people with low vision. The drawbacks of smartphones and tablets were text entry and touch operation difficulties and inaccessible apps for both blind and low vision users, problems in speech output for blind users, and problems in readability for low vision users. Researchers and makers of operating systems (OS) and apps should assume responsibility for solving these problems.

Smartphones have become vital devices in the current on-the-go Thai culture. Typically, virtual keyboards serve as tools for text input on smartphones. Due to the limited screen area and the large number of Thai characters, the size of each button on the keyboard is quite small. This leads to character mistyping and low typing speed. In this paper, we present a typical framework of a Thai Input Method on smartphones which includes four processes; Character Candidate Generation, Word Candidate Generation, Word Candidate Display, and Model Update. This framework not only works with Thai, it works with other letter-based languages as well. We also review virtual keyboards and techniques currently used and available for Thai text input.