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   Dupler, Ellen; Dunne, Lucy E. (January 2019, Proceedings of the 23rd International Symposium on Wearable Computers)
2. ChaoSen: Security Enhancement of Image Sensor through in-Sensor Chaotic Computing

   https://doi.org/10.1109/ICCD63220.2024.00012  

   Taheri, Nedasadat; Tabrizchi, Sepehr; Angizi, Shaahin; Roohi, Arman (November 2024, IEEE)
3. Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

   Park, Won; Liu, Nan; Chen, Qi Alfred; Mao, Z. Morley (January 2021, IEEE International Conference on Image Processing (ICIP))

   null (Ed.)

   A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the robustness of a high-performance, open source sensor fusion model architecture towards adversarial attacks and challenge the popular belief that the use of additional sensors automatically mitigate the risk of adversarial attacks. We find that despite the use of a LIDAR sensor, the model is vulnerable to our purposefully crafted image-based adversarial attacks including disappearance, universal patch, and spoofing. After identifying the underlying reason, we explore some potential defenses and provide some recommendations for improved sensor fusion models. 

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4. Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data

   Holder, Ryan C.; Sah, Ramesh K.; Cleveland, Michael J.; Ghasemzadeh, Hassan (January 2022, IEEE Consumer Communications and Networking Conference (CCNC))

   Detecting stress from wearable sensor data enables those struggling with unhealthy stress coping mechanisms to better manage their stress. Previous studies have investigated how mechanisms for detecting stress from sensor data can be optimized, comparing alternative algorithms and approaches to find the best possible outcome. One strategy to make these mechanisms more accessible is to reduce the number of sensors that wearable devices must support. Reducing the number of sensors will enable wearable devices to be a smaller size, require less battery, and last longer, making use of these wearable devices more accessible. To progress towards this more convenient stress detection mechanism, we investigate how learning algorithms perform on singular modalities and compare the outcome with results from multiple modalities. We found that singular modalities performed comparably or better than combined modalities on two stress-detection datasets, suggesting that there is promise for detecting stress with fewer sensor requirements. From the four modalities we tested, acceleration, blood volume pulse, and electrodermal activity, we saw acceleration and electrodermal activity to stand out in a few cases, but all modalities showed potential. Our results are acquired from testing with random holdout and leave-one-subject-out validation, using several machine learning techniques. Our results can inspire work on optimizing stress detection with singular modalities to make the benefits of these detection mechanisms more convenient. 

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5. Computational Sensor Fingerprints

   https://doi.org/10.1109/TIFS.2022.3179945  

   Korus, Pawel; Memon, Nasir (June 2022, IEEE Transactions on Information Forensics and Security)

   Analysis of imaging sensors is one of the most reliable photo forensic techniques, but it is increasingly chal- lenged by complex image processing in modern cameras. The underlying photo response non-uniformity (PRNU) is distilled into a static sensor fingerprint unique for each device. This makes it easy to estimate and spoof and limits its reliability in face of sophisticated attackers. We propose to exploit computa- tional capabilities of emerging intelligent vision sensors to design next-generation computational sensor fingerprints. Such sensors allow for running neural network inference directly on raw pixels, which enables end-to-end optimization of the entire photo acquisition and distribution pipeline. Control over fingerprint generation allows for adaptation to various requirements and threat models. In this study we provide a detailed assessment of security properties and evaluate two approaches to prevent spoofing: fingerprint generation based on local image content and adversarial training. We found that adversarial training is currently impractical, but content fingerprints deliver good per- formance in the considered cross-domain (RAW-RGB) setting and could provide robust best-effort protection against photo manip- ulation. Moreover, computational fingerprints can alleviate other limitations of PRNU, e.g., its limited reliability for dark/texture content and expensive fingerprint storage that hinders scalability. To enable this line of work, we developed a novel open-source and high-fidelity simulation environment for modeling photo acquisi- tion and distribution pipelines (https://github.com/pkorus/neural- imaging). 

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