This paper evaluates the performances of numerous encryption algorithms on mobile devices running the Android operating system. The primary objective of our research was to measure and compare the relative performances of tested algorithm implementations (Data Encryption Standard (DES), 3DES, Advanced Encryption Standard (AES), ChaCha20, Blowfish, and Rivest Cipher 4 (RC4)) on the Android platform. The algorithms were compared in terms of CPU utilization by measuring the time required to encrypt and decrypt variable size text files. Besides evaluating the six common symmetric encryption ciphers, a comparison has been conducted for several Password-Based Encryption (PBE) algorithms. Diverse cipher transformations were evaluated for each algorithm by utilizing various feedback modes and padding schemes. Two smartphone devices were used for testing, with different versions of the Android operating system and hardware specifications. The summarized performance outcomes for various cipher transformations are presented to demonstrate the effectiveness of each algorithm.

This paper deals with biometric identification based on unique patterns and characteristics of an individual’s handwriting, focusing on the dynamic writing process on a touchscreen device. Related work in this domain indicates the dominance of specific research approaches. Namely, in most cases, only the signature is analyzed, verification methods are more prevalent than recognition methods, and the provided solutions are mainly based on using a particular device or specific sensor for collecting biometric data. In this context, our work aims to fill the identified research gap by introducing a new handwriting-based user recognition technique. The proposed approach implements the concept of sensor fusion and does not rely exclusively on signatures for recognition but also includes other forms of handwriting, such as short sentences, words, or individual letters. Additionally, two different ways of handwriting input, using a stylus and a finger, are introduced into the analysis. In order to collect data on the dynamics of handwriting and signing, a specially designed apparatus was used with various sensors integrated into common smart devices, along with additional external sensors and accessories. A total of 60 participants took part in a controlled experiment to form a handwriting biometrics dataset for further analysis. To classify participants’ handwriting, custom architecture CNN models were utilized for feature extraction and classification tasks. The obtained results showed that the proposed handwriting recognition system achieves accuracies of 0.982, 0.927, 0.884, and 0.661 for signatures, words, short sentences, and individual letters, respectively. We further investigated the main effects of the input modality and the train set’s size on the system’s accuracy. Finally, an ablation study was carried out to analyze the impact of individual sensors within the fusion-based setup.

Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline and online. The first type involves the identification and interpretation of handwritten content obtained from an image, such as digitized human handwriting. The latter pertains to the identification of handwriting derived from digital writing performed on a touchpad or touchscreen. This research paper provides a comprehensive overview of the proposed apparatus specifically developed for collecting handwritten data. The acquisition of biometric information is conducted using a touchscreen device equipped with a variety of integrated and external sensors. In addition to acquiring signatures, the sensor-fusion-based configuration accumulates handwritten phrases, words, and individual letters to facilitate online user authentication. The proposed system can collect an extensive array of data. Specifically, it is possible to capture data related to stylus pressure, magnetometer readings, images, videos, and audio signals associated with handwriting executed on a tablet device. The study incorporates instances of gathered records, providing a graphical representation of the variation in handwriting among distinct users. The data obtained were additionally analyzed with regard to inter-person variability, intra-person variability, and classification potential. Initial findings from a limited sample of users demonstrate favorable results, intending to gather data from a more extensive user base.

This paper deals with biometric identification based on unique patterns and characteristics of an individual’s handwriting, focusing on the dynamic writing process on a touchscreen device. Related work in this domain indicates the dominance of specific research approaches. Namely, in most cases, only the signature is analyzed, verification methods are more prevalent than recognition methods, and the provided solutions are mainly based on using a particular device or specific sensor for collecting biometric data. In this context, our work aims to fill the identified research gap by introducing a new handwriting-based user recognition technique. The proposed approach implements the concept of sensor fusion and does not rely exclusively on signatures for recognition but also includes other forms of handwriting, such as short sentences, words, or individual letters. Additionally, two different ways of handwriting input, using a stylus and a finger, are introduced into the analysis. In order to collect data on the dynamics of handwriting and signing, a specially designed apparatus was used with various sensors integrated into common smart devices, along with additional external sensors and accessories. A total of 60 participants took part in a controlled experiment to form a handwriting biometrics dataset for further analysis. To classify participants’ handwriting, custom architecture CNN models were utilized for feature extraction and classification tasks. The obtained results showed that the proposed handwriting recognition system achieves accuracies of 0.982, 0.927, 0.884, and 0.661 for signatures, words, short sentences, and individual letters, respectively. We further investigated the main effects of the input modality and the train set’s size on the system’s accuracy. Finally, an ablation study was carried out to analyze the impact of individual sensors within the fusion-based setup.

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).