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1. Evaluating Continuous Authentication in Smartphones

   Guirguis, M.; Deb, D. (March 2021, 2021 Association of Computer Science Departments at Minority Institutions (ADMI) Symposium)

   null (Ed.)

   With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option. Specially, when the physical biometrics are challenging to utilize, and their frequent and continuous usage annoys the user. This paper presents a touchstroke authentication model based on several classification algorithms and compare their performances in authenticating legitimate smartphone users. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with an average accuracy of 91% considering the best performing model. This research is supervised by Dr. Debzani Deb (debd@wssu.edu), Department of Computer Science at Winston-Salem State University, NC. 

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2. AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

   https://doi.org/10.3390/s19245479  

   Rahnemoonfar, Maryam; Johnson, Jimmy; Paden, John (December 2019, Sensors)

   Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network. 

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3. Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion

   https://doi.org/10.1109/WIFS.2017.8267642  

   Li, Borui; Sun, Han; Gao, Yang; Phoha, Vir V.; Jin, Zhanpeng (December 2017, IEEE Workshop on Information Forensics and Security (WIFS), Rennes, France)

   Free-text keystroke is a form of behavioral biometrics which has great potential for addressing the security limitations of conventional one-time authentication by continuously monitoring the user's typing behaviors. This paper presents a new, enhanced continuous authentication approach by incorporating the dynamics of both keystrokes and wrist motions. Based upon two sets of features (free-text keystroke latency features and statistical wrist motion patterns extracted from the wrist-worn smartwatches), two one-vs-all Random Forest Ensemble Classifiers (RFECs) are constructed and trained respectively. A Dynamic Trust Model (DTM) is then developed to fuse the two classifiers' decisions and realize non-time-blocked real-time authentication. In the free-text typing experiments involving 25 human subjects, an imposter/intruder can be detected within no more than one sentence (average 56 keystrokes) with an FRR of 1.82% and an FAR of 1.94%. Compared with the scheme relying on only keystroke latency which has an FRR of 4.66%, an FAR of 17.92% and the required number of keystroke of 162, the proposed authentication system shows significant improvements in terms of accuracy, efficiency, and usability. 

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4. Self-Consuming Generative Models with Adversarially Curated Data

   Wei, Xiukun; Zhang, Xueru (July 2025, Forty-second International Conference on Machine Learning)

   Recent advances in generative models have made it increasingly difficult to distinguish real data from model-generated synthetic data. Using synthetic data for successive training of future model generations creates “self-consuming loops,” which may lead to model collapse or training instability. Furthermore, synthetic data is often subject to human feedback and curated by users based on their preferences. Ferbach et al. (2024) recently showed that when data is curated according to user preferences, the self-consuming retraining loop drives the model to converge toward a distribution that optimizes those preferences. However, in practice, data curation is often noisy or adversarially manipulated. For example, competing platforms may recruit malicious users to adversarially curate data and disrupt rival models. In this paper, we study how generative models evolve under self-consuming retraining loops with noisy and adversarially curated data. We theoretically analyze the impact of such noisy data curation on generative models and identify conditions for the robustness and stability of the retraining process. Building on this analysis, we design attack algorithms for competitive adversarial scenarios, where a platform with a limited budget employs malicious users to misalign a rival’s model from actual user preferences. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithms. 

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5. Mouse Dynamics Behavioral Biometrics: A Survey

   https://doi.org/10.1145/3640311  

   Khan, Simon; Devlen, Charles; Manno, Michael; Hou, Daqing (June 2024, ACM Computing Surveys)

   Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish individuals based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this article, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. We end the paper with presenting challenges and promising research opportunities. 

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