Computer Science Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1593

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Author: search.filters.author.Roy, NirupamStart date: 2023

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    “Is this my president speaking?” Tamper-proofing Speech in Live Recordings
    (Association for Computer Machinery (ACM), 2023-06-18) Shahid, Irtaza; Roy, Nirupam
    Malicious editing of audiovisual content has emerged as a popular tool for targeted defamation, spreading disinformation, and triggering political unrest. Public speeches and statements of political leaders, public figures, or celebrities are particularly at target due to their effectiveness in influencing the masses. Ubiquitous audiovisual recording of live speeches with smart devices and unrestricted content sharing and redistributing on social media make it difficult to address this threat using existing authentication techniques. Given public recordings of live events lack source control over the media, standard solutions falter. This paper presents TalkLock, a speech integrity verification system that can enable live speakers to protect their speeches from malicious alterations even when the speech is recorded by any member of the audience. The core idea is to generate meta-information from the speech signal in real-time and disseminate it through a secure QR code-based screen-camera communication. The QR code when recorded along with the speech embeds the meta-information in the content and it can be used later for independent verification in stand-alone applications or online platforms. A user study with live speech and real-world experiments with different types of voices, languages, environments, and distances show that TalkLock can verify fake content with 94.4% accuracy.
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    Structure Assisted Spectrum Sensing for Low-power Acoustic Event Detection
    (Association for Computer Machinery (ACM), 2023-05-09) Garg, Nakul; Takawale, Harshvardhan; Bai, Yang; Shahid, Irtaza; Roy, Nirupam
    Acoustic sensing has conventionally been dependent on highfrequency sampling of analog signals and frequency domain analysis in digital domain which is power-hungry. While these techniques work well for regular devices, low-power acoustic sensors demand for an alternative approach. In this work, we propose Lyra, a novel low-power acoustic sensing architecture that employs carefully designed passive structures to filter incoming sound waves and extract their frequency components. We eliminate power-hungry components such as ADC and digital FFT operations and instead propose to use low-power analog circuitry to process the signals. Lyra aims to provide a low-power platform for a range of maintenance-free acoustic event monitoring and ambient computing applications.
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    ThermWare: Toward Side-channel Defense for Tiny IoT Devices
    (Association for Computer Machinery (ACM), 2023-02-22) Garg, Nakul; Shahid, Irtaza; Avllazagaj, Erin; Hill, Jennie; Han, Jun; Roy, Nirupam
    As malware in IoT devices !ourishes, defenses are lacking. Traditional antivirus or intrusion detection-based defense techniques fail for the limited computational capabilities and the large diversity of platforms and environments. In this paper, we present ThermWare, a non-intrusive screening method to detect anomalous operations on embedded devices at run-time. ThermWare relies on the observation that electronic circuits generate subtle patterns of heat at the component level when the corresponding module is accessed by the micro-operations (e.g., file-write) of the running code. We propose the use of these side-channel heat signatures captured by a thermal camera to determine the sequence of underlying computations in real time. An early implementation of ThermWare shows success in detecting common malware routines in general-purpose IoT devices. We envision leveraging the thermal side-channel to track the internal operations of an embedded device, which can potentially lead to broader applications in engineering embedded systems, monitoring device health and run-time capacity, assisting embedded coding optimization, and physical layer security analysis.
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    Sirius: A Self-Localization System for Resource-Constrained IoT Sensors
    (Association for Computer Machinery (ACM), 2023-06-18) Garg, Nakul; Roy, Nirupam
    Low-power sensor networks are transforming large-scale sensing in precision farming, livestock tracking, climate-monitoring and surveying. Accurate and robust localization in such low-power sensor nodes has never been as crucial as it is today. This paper presents, Sirius, a self-localization system using a single receiver for low-power IoT nodes. Traditionally, systems have relied on antenna arrays and tight synchronization to estimate angle-of-arrival (AoA) and time-of-flight with known access points. While these techniques work well for regular mobile systems, low-power IoT nodes lack the resources to support these complex systems. Sirius explores the use of gain-pattern reconfigurable antennas with passive envelope detector-based radios to perform AoA estimation without requiring any kind of synchronization. It shows a technique to embed direction specific codes to the received signals which are transparent to regular communication channel but carry AoA information with them. Sirius embeds these direction-specific codes by using reconfigurable antennas and fluctuating the gain pattern of the antenna. Our prototype demonstrates a median error of 7 degrees in AoA estimation and 2.5 meters in localization, which is similar to state-of-the-art antenna array-based systems. Sirius opens up new possibilities for low-power IoT nodes.