Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing

Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we prop...
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Autor*in:	Di Xiao [verfasserIn] Yue Li [verfasserIn] Min Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	compressed sensing image reconstruction privacy preserving adversarial examples invertible

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 7, p 3575
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:7, p 3575

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23073575

Katalog-ID:	DOAJ089343247

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allfields	10.3390/s23073575 doi (DE-627)DOAJ089343247 (DE-599)DOAJa24c2c353d79403eaf59a9efb2ca4415 DE-627 ger DE-627 rakwb eng TP1-1185 Di Xiao verfasserin aut Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS. compressed sensing image reconstruction privacy preserving adversarial examples invertible Chemical technology Yue Li verfasserin aut Min Li verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7, p 3575 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7, p 3575 https://doi.org/10.3390/s23073575 kostenfrei https://doaj.org/article/a24c2c353d79403eaf59a9efb2ca4415 kostenfrei https://www.mdpi.com/1424-8220/23/7/3575 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 7, p 3575
spelling	10.3390/s23073575 doi (DE-627)DOAJ089343247 (DE-599)DOAJa24c2c353d79403eaf59a9efb2ca4415 DE-627 ger DE-627 rakwb eng TP1-1185 Di Xiao verfasserin aut Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS. compressed sensing image reconstruction privacy preserving adversarial examples invertible Chemical technology Yue Li verfasserin aut Min Li verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7, p 3575 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7, p 3575 https://doi.org/10.3390/s23073575 kostenfrei https://doaj.org/article/a24c2c353d79403eaf59a9efb2ca4415 kostenfrei https://www.mdpi.com/1424-8220/23/7/3575 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 7, p 3575
allfields_unstemmed	10.3390/s23073575 doi (DE-627)DOAJ089343247 (DE-599)DOAJa24c2c353d79403eaf59a9efb2ca4415 DE-627 ger DE-627 rakwb eng TP1-1185 Di Xiao verfasserin aut Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS. compressed sensing image reconstruction privacy preserving adversarial examples invertible Chemical technology Yue Li verfasserin aut Min Li verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7, p 3575 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7, p 3575 https://doi.org/10.3390/s23073575 kostenfrei https://doaj.org/article/a24c2c353d79403eaf59a9efb2ca4415 kostenfrei https://www.mdpi.com/1424-8220/23/7/3575 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 7, p 3575
allfieldsGer	10.3390/s23073575 doi (DE-627)DOAJ089343247 (DE-599)DOAJa24c2c353d79403eaf59a9efb2ca4415 DE-627 ger DE-627 rakwb eng TP1-1185 Di Xiao verfasserin aut Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS. compressed sensing image reconstruction privacy preserving adversarial examples invertible Chemical technology Yue Li verfasserin aut Min Li verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7, p 3575 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7, p 3575 https://doi.org/10.3390/s23073575 kostenfrei https://doaj.org/article/a24c2c353d79403eaf59a9efb2ca4415 kostenfrei https://www.mdpi.com/1424-8220/23/7/3575 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 7, p 3575
allfieldsSound	10.3390/s23073575 doi (DE-627)DOAJ089343247 (DE-599)DOAJa24c2c353d79403eaf59a9efb2ca4415 DE-627 ger DE-627 rakwb eng TP1-1185 Di Xiao verfasserin aut Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS. compressed sensing image reconstruction privacy preserving adversarial examples invertible Chemical technology Yue Li verfasserin aut Min Li verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7, p 3575 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7, p 3575 https://doi.org/10.3390/s23073575 kostenfrei https://doaj.org/article/a24c2c353d79403eaf59a9efb2ca4415 kostenfrei https://www.mdpi.com/1424-8220/23/7/3575 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 7, p 3575
language	English
source	In Sensors 23(2023), 7, p 3575 volume:23 year:2023 number:7, p 3575
sourceStr	In Sensors 23(2023), 7, p 3575 volume:23 year:2023 number:7, p 3575
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	compressed sensing image reconstruction privacy preserving adversarial examples invertible Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Di Xiao @@aut@@ Yue Li @@aut@@ Min Li @@aut@@
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callnumber-first	T - Technology
author	Di Xiao
spellingShingle	Di Xiao misc TP1-1185 misc compressed sensing misc image reconstruction misc privacy preserving misc adversarial examples misc invertible misc Chemical technology Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing
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topic_title	TP1-1185 Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing compressed sensing image reconstruction privacy preserving adversarial examples invertible
topic	misc TP1-1185 misc compressed sensing misc image reconstruction misc privacy preserving misc adversarial examples misc invertible misc Chemical technology
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title	Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing
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title_sort	invertible privacy-preserving adversarial reconstruction for image compressed sensing
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title_auth	Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing
abstract	Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS.
abstractGer	Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS.
abstract_unstemmed	Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS.
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title_short	Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing
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Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing

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