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1. 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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2. Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks

   https://doi.org/10.1109/GLOBECOM42002.2020.9348131  

   Yemini, Michal; Gil, Stephanie; Goldsmith, Andrea (December 2020, Globecomm)

   null (Ed.)
3. 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)
4. Unraveling Sensor Correlations in Multi-Sensor Wearable Devices for Smart Anomaly Detection

   https://doi.org/10.1109/DCAS61159.2024.10539883  

   Yasaei, Rozhin; Zargari, Amir_Hosein Afandizadeh; Al_Faruque, Mohammad; Kurdahi, Fadi (April 2024, IEEE)
5. Sensor Egregium—An Atomic Force Microscope Sensor for Continuously Variable Resonance Amplification

   https://doi.org/10.1115/1.4050274  

   Shihab, Rafiul; Jalil, Tasmirul; Gulsacan, Burak; Aureli, Matteo; Tung, Ryan (August 2021, Journal of Vibration and Acoustics)

   null (Ed.)

   Abstract Numerous nanometrology techniques concerned with probing a wide range of frequency-dependent properties would benefit from a cantilevered sensor with tunable natural frequencies. In this work, we propose a method to arbitrarily tune the stiffness and natural frequencies of a microplate sensor for atomic force microscope applications, thereby allowing resonance amplification at a broad range of frequencies. This method is predicated on the principle of curvature-based stiffening. A macroscale experiment is conducted to verify the feasibility of the method. Next, a microscale finite element analysis is conducted on a proof-of-concept device. We show that both the stiffness and various natural frequencies of the device can be controlled through applied transverse curvature. Dynamic phenomena encountered in the method, such as eigenvalue curve veering, are discussed and methods are presented to accommodate these phenomena. We believe that this study will facilitate the development of future curvature-based microscale sensors for atomic force microscopy applications. 

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