1.
MO Spata; VM Russo; A Ortis; S Battiato
A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm Journal Article
In: IEEE Access, vol. 13, pp. 24498 - 24514, 2025.
@article{Spata2025,
title = {A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm},
author = {MO Spata and VM Russo and A Ortis and S Battiato},
editor = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/10858134},
doi = {10.1109/ACCESS.2025.3536877},
year = {2025},
date = {2025-01-29},
journal = {IEEE Access},
volume = {13},
pages = {24498 - 24514},
abstract = {This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.
2.
Massimo Orazio Spata, Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato
Acoustic Side Channel Attack for Keystroke Splitting in the Wild Proceedings
2024, ISBN: 979-8-3503-7800-9.
@proceedings{Spata2024,
title = {Acoustic Side Channel Attack for Keystroke Splitting in the Wild},
author = {Massimo Orazio Spata, Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato},
editor = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/10796234},
doi = {10.1109/MetroXRAINE62247.2024.10796234},
isbn = {979-8-3503-7800-9},
year = {2024},
date = {2024-12-24},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {131-136},
abstract = {With the increasing prevalence of smart devices in our daily lives, concerns about security vulnerabilities have become more prominent. Acoustic Side Channel Attacks (ASCA) have emerged as a significant threat, exploiting sound emissions from devices to infer sensitive information such as keystrokes or conversations. We propose a novel approach leveraging deep learning techniques for audio segmentation in ASCA scenarios. Our method involves preprocessing the audio data, extracting relevant features, and applying a Temporal Convolutional Network (TCN) model for keystroke classification. We conducted experiments on a diverse dataset comprising various types of smart devices and attack scenarios. Our experiments demonstrate the effectiveness of the proposed audio segmentation method as fundamental preparatory step in ASCA attacks. The segmentation model achieved high precision, sensitivity, and specificity values, indicating its robustness in accurately. Furthermore, we observed consistent performance across different types of smart devices and attack scenarios, highlighting the generalizability of our approach in real conditions. The high precision, sensitivity, and specificity values obtained in our evaluation underscore the reliability and practical utility of the proposed approach. Experimental results demonstrate a peak accuracy of 990%, showcasing the effectiveness and precision of the approach. This high level of accuracy underscores the reliability and potential real-world applicability of the findings for keystroke splitting in the wild.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
With the increasing prevalence of smart devices in our daily lives, concerns about security vulnerabilities have become more prominent. Acoustic Side Channel Attacks (ASCA) have emerged as a significant threat, exploiting sound emissions from devices to infer sensitive information such as keystrokes or conversations. We propose a novel approach leveraging deep learning techniques for audio segmentation in ASCA scenarios. Our method involves preprocessing the audio data, extracting relevant features, and applying a Temporal Convolutional Network (TCN) model for keystroke classification. We conducted experiments on a diverse dataset comprising various types of smart devices and attack scenarios. Our experiments demonstrate the effectiveness of the proposed audio segmentation method as fundamental preparatory step in ASCA attacks. The segmentation model achieved high precision, sensitivity, and specificity values, indicating its robustness in accurately. Furthermore, we observed consistent performance across different types of smart devices and attack scenarios, highlighting the generalizability of our approach in real conditions. The high precision, sensitivity, and specificity values obtained in our evaluation underscore the reliability and practical utility of the proposed approach. Experimental results demonstrate a peak accuracy of 990%, showcasing the effectiveness and precision of the approach. This high level of accuracy underscores the reliability and potential real-world applicability of the findings for keystroke splitting in the wild.
3.
Dario Morganti, Maria Giovanna Rizzo, Massimo Orazio Spata, Salvatore Guglielmino, Barbara Fazio, Sebastiano Battiato, Sabrina Conoci
Temporal Convolutional Network on Raman Shift for Human Osteoblast Cells Fingerprint Analysis Journal Article
In: Intelligence-Based Medicine, vol. 10, 2024, ISSN: 2666-5212.
@article{Morganti2024,
title = {Temporal Convolutional Network on Raman Shift for Human Osteoblast Cells Fingerprint Analysis},
author = {Dario Morganti, Maria Giovanna Rizzo, Massimo Orazio Spata, Salvatore Guglielmino, Barbara Fazio, Sebastiano Battiato, Sabrina Conoci},
editor = {Elsevier},
url = {https://www.sciencedirect.com/science/article/pii/S2666521224000504},
doi = {https://doi.org/10.1016/j.ibmed.2024.100183},
issn = {2666-5212},
year = {2024},
date = {2024-10-24},
journal = {Intelligence-Based Medicine},
volume = {10},
abstract = {The physiological state and biological characteristics of cells play a crucial role in the study of several biological mechanisms that are at the basis of the life. Raman spectroscopy, a powerful non-destructive technique, has shown promise in providing unique molecular fingerprints of cells based on their vibrational states. However, the high-dimensional and noisy nature of Raman spectra poses significant challenges in precise cell classification. In this study, we present a novel deep learning algorithm tailored for human cells fingerprint assignment through Raman shift analysis. The proposed deep learning framework harnesses the power of Temporal Convolutional Networks (TCN) to efficiently extract and process Raman spectra information. Leveraging a dataset of labeled Raman spectra, the model is trained to learn discriminative features that capture the subtle differences in cell composition and molecular structures in differential states. Additionally, the proposed model enables real-time cell fingerprint prediction, making it highly applicable for high-throughput analysis in large-scale experiments. Experimental results demonstrate a peak accuracy of 99 %, showcasing the effectiveness and precision of the approach. Overall, the developed deep learning algorithm offers a robust and efficient solution for cell fingerprint assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The physiological state and biological characteristics of cells play a crucial role in the study of several biological mechanisms that are at the basis of the life. Raman spectroscopy, a powerful non-destructive technique, has shown promise in providing unique molecular fingerprints of cells based on their vibrational states. However, the high-dimensional and noisy nature of Raman spectra poses significant challenges in precise cell classification. In this study, we present a novel deep learning algorithm tailored for human cells fingerprint assignment through Raman shift analysis. The proposed deep learning framework harnesses the power of Temporal Convolutional Networks (TCN) to efficiently extract and process Raman spectra information. Leveraging a dataset of labeled Raman spectra, the model is trained to learn discriminative features that capture the subtle differences in cell composition and molecular structures in differential states. Additionally, the proposed model enables real-time cell fingerprint prediction, making it highly applicable for high-throughput analysis in large-scale experiments. Experimental results demonstrate a peak accuracy of 99 %, showcasing the effectiveness and precision of the approach. Overall, the developed deep learning algorithm offers a robust and efficient solution for cell fingerprint assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies.
4.
Massimo Orazio Spata, , Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato
A New Deep Learning Pipeline for Acoustic Attack on Keyboards Proceedings
vol. 1065, 2024, ISBN: 978-3-031-66328-4.
@proceedings{Spata2024b,
title = {A New Deep Learning Pipeline for Acoustic Attack on Keyboards},
author = {Massimo Orazio Spata, , Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato},
editor = {Springer},
url = {https://link.springer.com/chapter/10.1007/978-3-031-66329-1_26#citeas},
doi = {https://doi.org/10.1007/978-3-031-66329-1_26},
isbn = {978-3-031-66328-4},
year = {2024},
date = {2024-07-31},
booktitle = {Lecture Notes in Networks and Systems ((LNNS,volume 1065))},
volume = {1065},
pages = {402-414},
abstract = {The increasing reliance on services based on recent Artificial Intelligence advancements has elevated concerns about security vulnerabilities, leading to the exploration of novel attack vectors such as keystroke acoustic attacks on keyboards. This research delves into a deep learning approach for such attacks, which exploits acoustic emissions produced during typing to infer sensitive information. Traditional methods of keystroke acoustic attacks have relied on hand-engineered features and shallow classifiers, often failing to capture the intricate patterns within the acoustic data. In contrast, deep learning models have demonstrated remarkable capabilities in learning intricate patterns from complex data sources. We propose the exploitation of a Temporal Convolutional Network (TCN) to process acoustic signals, providing a more sophisticated and adaptive approach for keystroke acoustic attack analysis. The employed deep learning model showcases superior performance in multiple dimensions achieving a peak validation accuracy of 98.3% for keystrokes recorded by phone, and 93.05% for keystrokes recorded via Zoom, obtaining the best performances with respect the related prior art.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
The increasing reliance on services based on recent Artificial Intelligence advancements has elevated concerns about security vulnerabilities, leading to the exploration of novel attack vectors such as keystroke acoustic attacks on keyboards. This research delves into a deep learning approach for such attacks, which exploits acoustic emissions produced during typing to infer sensitive information. Traditional methods of keystroke acoustic attacks have relied on hand-engineered features and shallow classifiers, often failing to capture the intricate patterns within the acoustic data. In contrast, deep learning models have demonstrated remarkable capabilities in learning intricate patterns from complex data sources. We propose the exploitation of a Temporal Convolutional Network (TCN) to process acoustic signals, providing a more sophisticated and adaptive approach for keystroke acoustic attack analysis. The employed deep learning model showcases superior performance in multiple dimensions achieving a peak validation accuracy of 98.3% for keystrokes recorded by phone, and 93.05% for keystrokes recorded via Zoom, obtaining the best performances with respect the related prior art.
5.
M. Grisanti, M.A. Napoli Spatafora, A. Ortis, S. Battiato
E-ELPV: Extended ELPV Dataset For Accurate Solar Cells Defect Classification Conference Forthcoming
IntelliSys 2023, Forthcoming.
@conference{Grisanti2023,
title = {E-ELPV: Extended ELPV Dataset For Accurate Solar Cells Defect Classification},
author = {M. Grisanti, M.A. Napoli Spatafora, A. Ortis, S. Battiato},
year = {2023},
date = {2023-09-07},
booktitle = {IntelliSys 2023},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
6.
A. Rondinella, F. Guarnera, O. Giudice, A. Ortis, F. Rundo, S. Battiato
Attention-Based Convolutional Neural Network for CT Scan COVID-19 Detection Conference
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023.
@conference{Rondinella2023b,
title = {Attention-Based Convolutional Neural Network for CT Scan COVID-19 Detection},
author = {A. Rondinella, F. Guarnera, O. Giudice, A. Ortis, F. Rundo, S. Battiato
},
editor = {IEEE},
url = {http://iplab.dmi.unict.it/iplab/wp-content/uploads/2023/09/Attention-Based_Convolutional_Neural_Network_for_CT_Scan_COVID-19_Detection.pdf},
doi = {10.1109/ICASSPW59220.2023.10193471},
year = {2023},
date = {2023-06-04},
booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
7.
Francesco Rundo; Francesca Trenta; Roberto Leotta; Sebastiano Battiato
IMPROVE, 2021.
@conference{Rundo2021Deep,
title = {Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation},
author = {Francesco Rundo and Francesca Trenta and Roberto Leotta and Sebastiano Battiato},
url = {https://www.scitepress.org/Papers/2021/103809/103809.pdf},
year = {2021},
date = {2021-04-28},
booktitle = {IMPROVE},
pages = {75--80},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
8.
Francesco Rundo; Roberto Leotta; Francesca Trenta; Giovanni Bellitto; Federica Proietto Salanitri; Vincenzo Piuri; Angelo Genovese; Ruggero Donida Labati; Fabio Scotti; Concetto Spampinato; Sebastiano Battiato
IMPROVE, 2021.
@conference{Rundo2021advanced_c,
title = {Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving},
author = {Francesco Rundo and Roberto Leotta and Francesca Trenta and Giovanni Bellitto and Federica Proietto Salanitri and Vincenzo Piuri and Angelo Genovese and Ruggero Donida Labati and Fabio Scotti and Concetto Spampinato and Sebastiano Battiato},
url = {https://www.scitepress.org/Papers/2021/103810/103810.pdf},
year = {2021},
date = {2021-04-28},
booktitle = {IMPROVE},
pages = {81-90},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
9.
Francesco Rundo; Francesca Trenta; Roberto Leotta; Concetto Spampinato; Vincenzo Piuri; Sabrina Conoci; Ruggero Donida Labati; Fabio Scotti; Sebastiano Battiato
Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification Conference
ICPR Workshop 2020 - TC4 Workshop on Mobile and Wearable Biometrics (WMWB), vol. 12668, Springer 2021.
@conference{rundo2021advanced,
title = {Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification},
author = {Francesco Rundo and Francesca Trenta and Roberto Leotta and Concetto Spampinato and Vincenzo Piuri and Sabrina Conoci and Ruggero Donida Labati and Fabio Scotti and Sebastiano Battiato},
url = {https://air.unimi.it/retrieve/handle/2434/811655/1694134/Advanced_Temporal_1D_CNN_for_a_Robust_Car_Driver_Physiological_Identification.pdf},
year = {2021},
date = {2021-01-11},
booktitle = {ICPR Workshop 2020 - TC4 Workshop on Mobile and Wearable Biometrics (WMWB)},
volume = {12668},
pages = {184--199},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
10.
Francesco Rundo; Sabrina Conoci; Sebastiano Battiato; Francesca Trenta; Concetto Spampinato
AEIT Automotive 2020, 2020.
@conference{Rundo2020c,
title = {Innovative Saliency based Deep Driving Scene Understanding System for Automatic Safety Assessment in Next-Generation Cars},
author = {Francesco Rundo and Sabrina Conoci and Sebastiano Battiato and Francesca Trenta and Concetto Spampinato },
url = {https://convegni.aeit.it/auto2020/paper/1570659343.pdf},
year = {2020},
date = {2020-11-18},
booktitle = {AEIT Automotive 2020},
keywords = {},
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}
2025
MO Spata; VM Russo; A Ortis; S Battiato
A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm Journal Article
In: IEEE Access, vol. 13, pp. 24498 - 24514, 2025.
Abstract | Links | BibTeX | Tags: Acoustic side channel attack, Deep learning, laptop keystroke attacks, snooping keystroke attacks, user security and privacy, zoom-based acoustic attacks
@article{Spata2025,
title = {A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm},
author = {MO Spata and VM Russo and A Ortis and S Battiato},
editor = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/10858134},
doi = {10.1109/ACCESS.2025.3536877},
year = {2025},
date = {2025-01-29},
journal = {IEEE Access},
volume = {13},
pages = {24498 - 24514},
abstract = {This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.},
keywords = {Acoustic side channel attack, Deep learning, laptop keystroke attacks, snooping keystroke attacks, user security and privacy, zoom-based acoustic attacks},
pubstate = {published},
tppubtype = {article}
}
This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.
2024
Massimo Orazio Spata, Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato
Acoustic Side Channel Attack for Keystroke Splitting in the Wild Proceedings
2024, ISBN: 979-8-3503-7800-9.
Abstract | Links | BibTeX | Tags: Acoustic side channel attack, Deep learning, laptop keystroke attacks, user security and privacy
@proceedings{Spata2024,
title = {Acoustic Side Channel Attack for Keystroke Splitting in the Wild},
author = {Massimo Orazio Spata, Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato},
editor = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/10796234},
doi = {10.1109/MetroXRAINE62247.2024.10796234},
isbn = {979-8-3503-7800-9},
year = {2024},
date = {2024-12-24},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {131-136},
abstract = {With the increasing prevalence of smart devices in our daily lives, concerns about security vulnerabilities have become more prominent. Acoustic Side Channel Attacks (ASCA) have emerged as a significant threat, exploiting sound emissions from devices to infer sensitive information such as keystrokes or conversations. We propose a novel approach leveraging deep learning techniques for audio segmentation in ASCA scenarios. Our method involves preprocessing the audio data, extracting relevant features, and applying a Temporal Convolutional Network (TCN) model for keystroke classification. We conducted experiments on a diverse dataset comprising various types of smart devices and attack scenarios. Our experiments demonstrate the effectiveness of the proposed audio segmentation method as fundamental preparatory step in ASCA attacks. The segmentation model achieved high precision, sensitivity, and specificity values, indicating its robustness in accurately. Furthermore, we observed consistent performance across different types of smart devices and attack scenarios, highlighting the generalizability of our approach in real conditions. The high precision, sensitivity, and specificity values obtained in our evaluation underscore the reliability and practical utility of the proposed approach. Experimental results demonstrate a peak accuracy of 990%, showcasing the effectiveness and precision of the approach. This high level of accuracy underscores the reliability and potential real-world applicability of the findings for keystroke splitting in the wild.},
keywords = {Acoustic side channel attack, Deep learning, laptop keystroke attacks, user security and privacy},
pubstate = {published},
tppubtype = {proceedings}
}
With the increasing prevalence of smart devices in our daily lives, concerns about security vulnerabilities have become more prominent. Acoustic Side Channel Attacks (ASCA) have emerged as a significant threat, exploiting sound emissions from devices to infer sensitive information such as keystrokes or conversations. We propose a novel approach leveraging deep learning techniques for audio segmentation in ASCA scenarios. Our method involves preprocessing the audio data, extracting relevant features, and applying a Temporal Convolutional Network (TCN) model for keystroke classification. We conducted experiments on a diverse dataset comprising various types of smart devices and attack scenarios. Our experiments demonstrate the effectiveness of the proposed audio segmentation method as fundamental preparatory step in ASCA attacks. The segmentation model achieved high precision, sensitivity, and specificity values, indicating its robustness in accurately. Furthermore, we observed consistent performance across different types of smart devices and attack scenarios, highlighting the generalizability of our approach in real conditions. The high precision, sensitivity, and specificity values obtained in our evaluation underscore the reliability and practical utility of the proposed approach. Experimental results demonstrate a peak accuracy of 990%, showcasing the effectiveness and precision of the approach. This high level of accuracy underscores the reliability and potential real-world applicability of the findings for keystroke splitting in the wild.
Dario Morganti, Maria Giovanna Rizzo, Massimo Orazio Spata, Salvatore Guglielmino, Barbara Fazio, Sebastiano Battiato, Sabrina Conoci
Temporal Convolutional Network on Raman Shift for Human Osteoblast Cells Fingerprint Analysis Journal Article
In: Intelligence-Based Medicine, vol. 10, 2024, ISSN: 2666-5212.
Abstract | Links | BibTeX | Tags: Deep learning, Human osteoblast cells, Raman spectroscopy
@article{Morganti2024,
title = {Temporal Convolutional Network on Raman Shift for Human Osteoblast Cells Fingerprint Analysis},
author = {Dario Morganti, Maria Giovanna Rizzo, Massimo Orazio Spata, Salvatore Guglielmino, Barbara Fazio, Sebastiano Battiato, Sabrina Conoci},
editor = {Elsevier},
url = {https://www.sciencedirect.com/science/article/pii/S2666521224000504},
doi = {https://doi.org/10.1016/j.ibmed.2024.100183},
issn = {2666-5212},
year = {2024},
date = {2024-10-24},
journal = {Intelligence-Based Medicine},
volume = {10},
abstract = {The physiological state and biological characteristics of cells play a crucial role in the study of several biological mechanisms that are at the basis of the life. Raman spectroscopy, a powerful non-destructive technique, has shown promise in providing unique molecular fingerprints of cells based on their vibrational states. However, the high-dimensional and noisy nature of Raman spectra poses significant challenges in precise cell classification. In this study, we present a novel deep learning algorithm tailored for human cells fingerprint assignment through Raman shift analysis. The proposed deep learning framework harnesses the power of Temporal Convolutional Networks (TCN) to efficiently extract and process Raman spectra information. Leveraging a dataset of labeled Raman spectra, the model is trained to learn discriminative features that capture the subtle differences in cell composition and molecular structures in differential states. Additionally, the proposed model enables real-time cell fingerprint prediction, making it highly applicable for high-throughput analysis in large-scale experiments. Experimental results demonstrate a peak accuracy of 99 %, showcasing the effectiveness and precision of the approach. Overall, the developed deep learning algorithm offers a robust and efficient solution for cell fingerprint assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies.},
keywords = {Deep learning, Human osteoblast cells, Raman spectroscopy},
pubstate = {published},
tppubtype = {article}
}
The physiological state and biological characteristics of cells play a crucial role in the study of several biological mechanisms that are at the basis of the life. Raman spectroscopy, a powerful non-destructive technique, has shown promise in providing unique molecular fingerprints of cells based on their vibrational states. However, the high-dimensional and noisy nature of Raman spectra poses significant challenges in precise cell classification. In this study, we present a novel deep learning algorithm tailored for human cells fingerprint assignment through Raman shift analysis. The proposed deep learning framework harnesses the power of Temporal Convolutional Networks (TCN) to efficiently extract and process Raman spectra information. Leveraging a dataset of labeled Raman spectra, the model is trained to learn discriminative features that capture the subtle differences in cell composition and molecular structures in differential states. Additionally, the proposed model enables real-time cell fingerprint prediction, making it highly applicable for high-throughput analysis in large-scale experiments. Experimental results demonstrate a peak accuracy of 99 %, showcasing the effectiveness and precision of the approach. Overall, the developed deep learning algorithm offers a robust and efficient solution for cell fingerprint assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies.
Massimo Orazio Spata, , Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato
A New Deep Learning Pipeline for Acoustic Attack on Keyboards Proceedings
vol. 1065, 2024, ISBN: 978-3-031-66328-4.
Abstract | Links | BibTeX | Tags: Acoustic side channel attack, Deep learning, laptop keystroke attacks, user security and privacy, zoom-based acoustic attacks
@proceedings{Spata2024b,
title = {A New Deep Learning Pipeline for Acoustic Attack on Keyboards},
author = {Massimo Orazio Spata, , Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato},
editor = {Springer},
url = {https://link.springer.com/chapter/10.1007/978-3-031-66329-1_26#citeas},
doi = {https://doi.org/10.1007/978-3-031-66329-1_26},
isbn = {978-3-031-66328-4},
year = {2024},
date = {2024-07-31},
booktitle = {Lecture Notes in Networks and Systems ((LNNS,volume 1065))},
volume = {1065},
pages = {402-414},
abstract = {The increasing reliance on services based on recent Artificial Intelligence advancements has elevated concerns about security vulnerabilities, leading to the exploration of novel attack vectors such as keystroke acoustic attacks on keyboards. This research delves into a deep learning approach for such attacks, which exploits acoustic emissions produced during typing to infer sensitive information. Traditional methods of keystroke acoustic attacks have relied on hand-engineered features and shallow classifiers, often failing to capture the intricate patterns within the acoustic data. In contrast, deep learning models have demonstrated remarkable capabilities in learning intricate patterns from complex data sources. We propose the exploitation of a Temporal Convolutional Network (TCN) to process acoustic signals, providing a more sophisticated and adaptive approach for keystroke acoustic attack analysis. The employed deep learning model showcases superior performance in multiple dimensions achieving a peak validation accuracy of 98.3% for keystrokes recorded by phone, and 93.05% for keystrokes recorded via Zoom, obtaining the best performances with respect the related prior art.},
keywords = {Acoustic side channel attack, Deep learning, laptop keystroke attacks, user security and privacy, zoom-based acoustic attacks},
pubstate = {published},
tppubtype = {proceedings}
}
The increasing reliance on services based on recent Artificial Intelligence advancements has elevated concerns about security vulnerabilities, leading to the exploration of novel attack vectors such as keystroke acoustic attacks on keyboards. This research delves into a deep learning approach for such attacks, which exploits acoustic emissions produced during typing to infer sensitive information. Traditional methods of keystroke acoustic attacks have relied on hand-engineered features and shallow classifiers, often failing to capture the intricate patterns within the acoustic data. In contrast, deep learning models have demonstrated remarkable capabilities in learning intricate patterns from complex data sources. We propose the exploitation of a Temporal Convolutional Network (TCN) to process acoustic signals, providing a more sophisticated and adaptive approach for keystroke acoustic attack analysis. The employed deep learning model showcases superior performance in multiple dimensions achieving a peak validation accuracy of 98.3% for keystrokes recorded by phone, and 93.05% for keystrokes recorded via Zoom, obtaining the best performances with respect the related prior art.
2023
M. Grisanti, M.A. Napoli Spatafora, A. Ortis, S. Battiato
E-ELPV: Extended ELPV Dataset For Accurate Solar Cells Defect Classification Conference Forthcoming
IntelliSys 2023, Forthcoming.
BibTeX | Tags: Convolutional Neural Networks, Deep learning, Defect Classification, Defect Detection, Electroluminescence Imaging, Solar Energy, Solar Modules, Visual Inspection
@conference{Grisanti2023,
title = {E-ELPV: Extended ELPV Dataset For Accurate Solar Cells Defect Classification},
author = {M. Grisanti, M.A. Napoli Spatafora, A. Ortis, S. Battiato},
year = {2023},
date = {2023-09-07},
booktitle = {IntelliSys 2023},
keywords = {Convolutional Neural Networks, Deep learning, Defect Classification, Defect Detection, Electroluminescence Imaging, Solar Energy, Solar Modules, Visual Inspection},
pubstate = {forthcoming},
tppubtype = {conference}
}
A. Rondinella, F. Guarnera, O. Giudice, A. Ortis, F. Rundo, S. Battiato
Attention-Based Convolutional Neural Network for CT Scan COVID-19 Detection Conference
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023.
Links | BibTeX | Tags: Computed Tomography classification, Covid-19 detection, Deep learning, Medical Imaging
@conference{Rondinella2023b,
title = {Attention-Based Convolutional Neural Network for CT Scan COVID-19 Detection},
author = {A. Rondinella, F. Guarnera, O. Giudice, A. Ortis, F. Rundo, S. Battiato
},
editor = {IEEE},
url = {http://iplab.dmi.unict.it/iplab/wp-content/uploads/2023/09/Attention-Based_Convolutional_Neural_Network_for_CT_Scan_COVID-19_Detection.pdf},
doi = {10.1109/ICASSPW59220.2023.10193471},
year = {2023},
date = {2023-06-04},
booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
keywords = {Computed Tomography classification, Covid-19 detection, Deep learning, Medical Imaging},
pubstate = {published},
tppubtype = {conference}
}
2021
Francesco Rundo; Francesca Trenta; Roberto Leotta; Sebastiano Battiato
IMPROVE, 2021.
Links | BibTeX | Tags: Computer Vision, Deep learning, PPG (PhotoPlethysmoGraphy
@conference{Rundo2021Deep,
title = {Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation},
author = {Francesco Rundo and Francesca Trenta and Roberto Leotta and Sebastiano Battiato},
url = {https://www.scitepress.org/Papers/2021/103809/103809.pdf},
year = {2021},
date = {2021-04-28},
booktitle = {IMPROVE},
pages = {75--80},
keywords = {Computer Vision, Deep learning, PPG (PhotoPlethysmoGraphy},
pubstate = {published},
tppubtype = {conference}
}
Francesco Rundo; Roberto Leotta; Francesca Trenta; Giovanni Bellitto; Federica Proietto Salanitri; Vincenzo Piuri; Angelo Genovese; Ruggero Donida Labati; Fabio Scotti; Concetto Spampinato; Sebastiano Battiato
IMPROVE, 2021.
Links | BibTeX | Tags: D-CNN, Deep learning, Deep-LSTM, Drowsiness, PPG (PhotoPlethySmography)
@conference{Rundo2021advanced_c,
title = {Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving},
author = {Francesco Rundo and Roberto Leotta and Francesca Trenta and Giovanni Bellitto and Federica Proietto Salanitri and Vincenzo Piuri and Angelo Genovese and Ruggero Donida Labati and Fabio Scotti and Concetto Spampinato and Sebastiano Battiato},
url = {https://www.scitepress.org/Papers/2021/103810/103810.pdf},
year = {2021},
date = {2021-04-28},
booktitle = {IMPROVE},
pages = {81-90},
keywords = {D-CNN, Deep learning, Deep-LSTM, Drowsiness, PPG (PhotoPlethySmography)},
pubstate = {published},
tppubtype = {conference}
}
Francesco Rundo; Francesca Trenta; Roberto Leotta; Concetto Spampinato; Vincenzo Piuri; Sabrina Conoci; Ruggero Donida Labati; Fabio Scotti; Sebastiano Battiato
Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification Conference
ICPR Workshop 2020 - TC4 Workshop on Mobile and Wearable Biometrics (WMWB), vol. 12668, Springer 2021.
Links | BibTeX | Tags: ADAS, automotive, Deep learning
@conference{rundo2021advanced,
title = {Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification},
author = {Francesco Rundo and Francesca Trenta and Roberto Leotta and Concetto Spampinato and Vincenzo Piuri and Sabrina Conoci and Ruggero Donida Labati and Fabio Scotti and Sebastiano Battiato},
url = {https://air.unimi.it/retrieve/handle/2434/811655/1694134/Advanced_Temporal_1D_CNN_for_a_Robust_Car_Driver_Physiological_Identification.pdf},
year = {2021},
date = {2021-01-11},
booktitle = {ICPR Workshop 2020 - TC4 Workshop on Mobile and Wearable Biometrics (WMWB)},
volume = {12668},
pages = {184--199},
organization = {Springer},
keywords = {ADAS, automotive, Deep learning},
pubstate = {published},
tppubtype = {conference}
}
2020
Francesco Rundo; Sabrina Conoci; Sebastiano Battiato; Francesca Trenta; Concetto Spampinato
AEIT Automotive 2020, 2020.
Links | BibTeX | Tags: D-CNN, Deep learning, DeepLSTM, Drowsiness, PPG (PhotoPlethySmography)
@conference{Rundo2020c,
title = {Innovative Saliency based Deep Driving Scene Understanding System for Automatic Safety Assessment in Next-Generation Cars},
author = {Francesco Rundo and Sabrina Conoci and Sebastiano Battiato and Francesca Trenta and Concetto Spampinato },
url = {https://convegni.aeit.it/auto2020/paper/1570659343.pdf},
year = {2020},
date = {2020-11-18},
booktitle = {AEIT Automotive 2020},
keywords = {D-CNN, Deep learning, DeepLSTM, Drowsiness, PPG (PhotoPlethySmography)},
pubstate = {published},
tppubtype = {conference}
}