Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels

Carmen Veenker; Danny Opdam; Mina Alishahi

doi:10.1007/978-3-031-54129-2_33

Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels

Carmen Veenker, Danny Opdam, Mina Alishahi^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Abstract

In recent years, face recognition as an effective biometric technique has received significant attention. The worldwide application of face recognition, however, raises strong concerns regarding the individuals’ privacy. This problem has been particularly addressed by encrypting all pixels of an image, which is computationally heavy. In this paper, we propose a new framework using deep neural network that determines the most identifiable pixels (instead of all pixels) of an image for encryption. Our experimental result shows that our proposed approach significantly reduces the number of pixels required to be encrypted.

Original language	English
Title of host publication	Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers
Editors	Sokratis Katsikas, Habtamu Abie, Silvio Ranise, Luca Verderame, Enrico Cambiaso, Rita Ugarelli, Isabel Praça, Wenjuan Li, Weizhi Meng, Steven Furnell, Basel Katt, Sandeep Pirbhulal, Ankur Shukla, Michele Ianni, Mila Dalla Preda, Kim-Kwang Raymond Choo, Miguel Pupo Correia, Abhishta Abhishta, Giovanni Sileno, Mina Alishahi, Harsha Kalutarage, Naoto Yanai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	552-568
Number of pages	17
ISBN (Print)	9783031541285
DOIs	https://doi.org/10.1007/978-3-031-54129-2_33
Publication status	Published - 2024
Event	International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security - The Hague, Netherlands Duration: 25 Sept 2023 → 29 Sept 2023 https://esorics2023.org/program/workshop/

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14399 LNCS
ISSN	0302-9743

Workshop

Workshop	International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security
Abbreviated title	ESORICS 2023
Country/Territory	Netherlands
City	The Hague
Period	25/09/23 → 29/09/23
Internet address	https://esorics2023.org/program/workshop/

Keywords

Deep Learning
Encryption
Face Recognition
Privacy

Access to Document

10.1007/978-3-031-54129-2_33

Cite this

Veenker, C., Opdam, D., & Alishahi, M. (2024). Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels. In S. Katsikas, H. Abie, S. Ranise, L. Verderame, E. Cambiaso, R. Ugarelli, I. Praça, W. Li, W. Meng, S. Furnell, B. Katt, S. Pirbhulal, A. Shukla, M. Ianni, M. Dalla Preda, K.-K. R. Choo, M. Pupo Correia, A. Abhishta, G. Sileno, M. Alishahi, H. Kalutarage, ... N. Yanai (Eds.), Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers (pp. 552-568). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-54129-2_33

Veenker, Carmen ; Opdam, Danny ; Alishahi, Mina. / Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels. Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers. editor / Sokratis Katsikas ; Habtamu Abie ; Silvio Ranise ; Luca Verderame ; Enrico Cambiaso ; Rita Ugarelli ; Isabel Praça ; Wenjuan Li ; Weizhi Meng ; Steven Furnell ; Basel Katt ; Sandeep Pirbhulal ; Ankur Shukla ; Michele Ianni ; Mila Dalla Preda ; Kim-Kwang Raymond Choo ; Miguel Pupo Correia ; Abhishta Abhishta ; Giovanni Sileno ; Mina Alishahi ; Harsha Kalutarage ; Naoto Yanai. Springer Science and Business Media Deutschland GmbH, 2024. pp. 552-568 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14399 LNCS).

@inproceedings{e11db679ced24488a112f2844aa96e8a,

title = "Pixels Who Violate Our Privacy! Deep Learning for Identifying Images{\textquoteright} Key Pixels",

abstract = "In recent years, face recognition as an effective biometric technique has received significant attention. The worldwide application of face recognition, however, raises strong concerns regarding the individuals{\textquoteright} privacy. This problem has been particularly addressed by encrypting all pixels of an image, which is computationally heavy. In this paper, we propose a new framework using deep neural network that determines the most identifiable pixels (instead of all pixels) of an image for encryption. Our experimental result shows that our proposed approach significantly reduces the number of pixels required to be encrypted.",

keywords = "Deep Learning, Encryption, Face Recognition, Privacy",

author = "Carmen Veenker and Danny Opdam and Mina Alishahi",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security, ESORICS 2023 ; Conference date: 25-09-2023 Through 29-09-2023",

year = "2024",

doi = "10.1007/978-3-031-54129-2_33",

language = "English",

isbn = "9783031541285",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "552--568",

editor = "Sokratis Katsikas and Habtamu Abie and Silvio Ranise and Luca Verderame and Enrico Cambiaso and Rita Ugarelli and Isabel Pra{\c c}a and Wenjuan Li and Weizhi Meng and Steven Furnell and Basel Katt and Sandeep Pirbhulal and Ankur Shukla and Michele Ianni and {Dalla Preda}, Mila and Choo, {Kim-Kwang Raymond} and {Pupo Correia}, Miguel and Abhishta Abhishta and Giovanni Sileno and Mina Alishahi and Harsha Kalutarage and Naoto Yanai",

booktitle = "Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers",

address = "Germany",

url = "https://esorics2023.org/program/workshop/",

}

Veenker, C, Opdam, D & Alishahi, M 2024, Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels. in S Katsikas, H Abie, S Ranise, L Verderame, E Cambiaso, R Ugarelli, I Praça, W Li, W Meng, S Furnell, B Katt, S Pirbhulal, A Shukla, M Ianni, M Dalla Preda, K-KR Choo, M Pupo Correia, A Abhishta, G Sileno, M Alishahi, H Kalutarage & N Yanai (eds), Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14399 LNCS, pp. 552-568, International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security, The Hague, Netherlands, 25/09/23. https://doi.org/10.1007/978-3-031-54129-2_33

Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels. / Veenker, Carmen; Opdam, Danny; Alishahi, Mina.
Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers. ed. / Sokratis Katsikas; Habtamu Abie; Silvio Ranise; Luca Verderame; Enrico Cambiaso; Rita Ugarelli; Isabel Praça; Wenjuan Li; Weizhi Meng; Steven Furnell; Basel Katt; Sandeep Pirbhulal; Ankur Shukla; Michele Ianni; Mila Dalla Preda; Kim-Kwang Raymond Choo; Miguel Pupo Correia; Abhishta Abhishta; Giovanni Sileno; Mina Alishahi; Harsha Kalutarage; Naoto Yanai. Springer Science and Business Media Deutschland GmbH, 2024. p. 552-568 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14399 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels

AU - Veenker, Carmen

AU - Opdam, Danny

AU - Alishahi, Mina

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - In recent years, face recognition as an effective biometric technique has received significant attention. The worldwide application of face recognition, however, raises strong concerns regarding the individuals’ privacy. This problem has been particularly addressed by encrypting all pixels of an image, which is computationally heavy. In this paper, we propose a new framework using deep neural network that determines the most identifiable pixels (instead of all pixels) of an image for encryption. Our experimental result shows that our proposed approach significantly reduces the number of pixels required to be encrypted.

AB - In recent years, face recognition as an effective biometric technique has received significant attention. The worldwide application of face recognition, however, raises strong concerns regarding the individuals’ privacy. This problem has been particularly addressed by encrypting all pixels of an image, which is computationally heavy. In this paper, we propose a new framework using deep neural network that determines the most identifiable pixels (instead of all pixels) of an image for encryption. Our experimental result shows that our proposed approach significantly reduces the number of pixels required to be encrypted.

KW - Deep Learning

KW - Encryption

KW - Face Recognition

KW - Privacy

U2 - 10.1007/978-3-031-54129-2_33

DO - 10.1007/978-3-031-54129-2_33

M3 - Conference Article in proceeding

AN - SCOPUS:85188733978

SN - 9783031541285

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 552

EP - 568

BT - Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers

A2 - Katsikas, Sokratis

A2 - Abie, Habtamu

A2 - Ranise, Silvio

A2 - Verderame, Luca

A2 - Cambiaso, Enrico

A2 - Ugarelli, Rita

A2 - Praça, Isabel

A2 - Li, Wenjuan

A2 - Meng, Weizhi

A2 - Furnell, Steven

A2 - Katt, Basel

A2 - Pirbhulal, Sandeep

A2 - Shukla, Ankur

A2 - Ianni, Michele

A2 - Dalla Preda, Mila

A2 - Choo, Kim-Kwang Raymond

A2 - Pupo Correia, Miguel

A2 - Abhishta, Abhishta

A2 - Sileno, Giovanni

A2 - Alishahi, Mina

A2 - Kalutarage, Harsha

A2 - Yanai, Naoto

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security

Y2 - 25 September 2023 through 29 September 2023

ER -

Veenker C, Opdam D, Alishahi M. Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels. In Katsikas S, Abie H, Ranise S, Verderame L, Cambiaso E, Ugarelli R, Praça I, Li W, Meng W, Furnell S, Katt B, Pirbhulal S, Shukla A, Ianni M, Dalla Preda M, Choo KKR, Pupo Correia M, Abhishta A, Sileno G, Alishahi M, Kalutarage H, Yanai N, editors, Computer Security. ESORICS 2023 International Workshops - CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI, 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 552-568. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14399 LNCS). doi: 10.1007/978-3-031-54129-2_33

Pixels Who Violate Our Privacy! Deep Learning for Identifying Images’ Key Pixels

Abstract

Publication series

Workshop

Keywords

Access to Document

Fingerprint

Cite this