With AI-generated content becoming widespread across digital platforms, it is important to understand how such content is inspired and produced. This study explores the underexamined task of image regeneration, where a human operator iteratively refines prompts to recreate a specific target image. Unlike typical image generation, regeneration begins with a visual reference. A key challenge is whether existing image similarity metrics (ISMs) align with human judgments and can serve as useful feedback in this process. We conduct a structured user study to evaluate how iterative prompt refinement affects similarity to target images and whether ISMs reflect the improvements perceived by human observers. Our results show that prompt adjustments significantly improve alignment, both subjectively and quantitatively, highlighting the potential of iterative workflows in enhancing generative image quality.

E-scooter rental services have significantly expanded the micromobility paradigm of short-distance urban and suburban transportation since their inception in 2017. Service providers around the world have followed a common rental model wherein customers (i.e., riders or users) download and install a mobile application for locating (finding) and renting e-scooters. Unlike many other app categories, e-scooter rental apps require a set of privacy-sensitive user data as a functional requirement. Unfortunately, privacy-related questions such as how much user data is being collected by these apps, is user data being safely handled once acquired, and with whom the collected user data is being shared are not readily known to customers. Answering such questions can be critical for users in determining which e-scooter rental services are sufficiently trustworthy per their personal privacy preferences. In this paper, we conduct a comprehensive analysis of e-scooter rental apps to answer these and other research questions related to user data collection, third-party involvement, usefulness of privacy policies, and evolution of user data management by different e-scooter apps/services over time. Our findings will create awareness among consumers vis-à-vis the data they share with service providers in return for the received e-scooter rental service, and it can also evoke more accountability and transparency from service providers towards their efforts and processes on protecting consumer privacy.

The popularity of smart wrist wearable technology (e.g., smart-watches) has rejuvenated the exploration of dynamic biometric-based authentication techniques that employ sensor data from these devices. Despite the progress demonstrated by the scientific community, research in this area has not successfully transitioned to practice, and we are yet to see a mainstream user-authentication product based on a dynamic biometric such as handwriting/hand gestures captured using commercial wrist wearables. This work undertakes an investigative analysis to further explore why that is the case. We accomplish this by studying the feasibility and practical deployability of handwriting-based authentication techniques in the literature that utilize motion sensors on-board wrist wearables. We conduct this analysis by replicating four state-of-the-art and representative handwriting-based authentication schemes that employ wrist motion data, in order to test their viability in realistic hand-writing/gesture scenarios. By using data collected from actual human subjects in an unconstrained fashion, we comparatively evaluate the performance of these schemes with well-defined usability and security metrics. Our experimental results show that some of the tested schemes perform considerably well in practice, and are promising. However, they do suffer from several practical user-dependent and technique-specific challenges that act as roadblocks towards their wide-scale adoption in mainstream applications.

Several recent research efforts have shown that privacy of handwritten information is vulnerable to inference threats that employ zero-permission motion sensors commonly found on wrist-wearables (e.g., smart watches and fitness bands) as information side-channels. While the adversary model in these earlier efforts have been reasonable and the proposed inference (or threat) frameworks themselves are practical and have technical merit, the related empirical evaluations suffer from several significant shortcomings, such as, use of specialized sensor hardware and highly constrained or restrictive experimental procedures, to name a few. As a result, it is hard to estimate the practical feasibility of these threats from existing research results in the literature, and thus, the extent to which end-users must be concerned about the possibility of such attacks in real-life. To answer the above question, this paper replicates some of the well-known wrist motion-based handwriting inference frameworks in the literature in order to (re)evaluate their success or accuracy in natural, unrestricted handwriting scenarios and settings by employing commercially available wrist-wearables. The results of these extensive replication and (re)evaluation studies highlight several characteristics in motion data corresponding to natural handwriting scenarios, which were either not observed or ignored by earlier efforts, and contribute to poor inference accuracy of the corresponding frameworks. In summary, accurate and practical handwriting inference using motion data (side-channeled) from consumer-grade wrist-wearables is difficult primarily due to unique and/or inconsistent handwriting behavior observed in natural writing.