Human-Imperceptible Identification with Learnable Lensless Imaging
Lensless imaging protects visual privacy by capturing heavily blurred images that are imperceptible for humans to recognize the subject but contain enough information for machines to infer information. Unfortunately, protecting visual privacy comes with a reduction in recognition accuracy and vice v...
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| Veröffentlicht in: | IEEE access 2023-01, Vol.11, p.1-1 |
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| creator | Canh, Thuong Nguyen Ngo, Trung Thanh Nagahara, Haijme |
| description | Lensless imaging protects visual privacy by capturing heavily blurred images that are imperceptible for humans to recognize the subject but contain enough information for machines to infer information. Unfortunately, protecting visual privacy comes with a reduction in recognition accuracy and vice versa. We propose a learnable lensless imaging framework that protects visual privacy while maintaining recognition accuracy. To make captured images imperceptible to humans, we designed several loss functions based on total variation, invertibility, and the restricted isometry property. We studied the effect of privacy protection with blurriness on the identification of personal identity via a quantitative method based on a subjective evaluation. Moreover, we validate our simulation by implementing a hardware realization of lensless imaging with photo-lithographically printed masks. |
| doi_str_mv | 10.1109/ACCESS.2023.3308069 |
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Moreover, we validate our simulation by implementing a hardware realization of lensless imaging with photo-lithographically printed masks.</description><subject>Cameras</subject><subject>Compressive Sensing</subject><subject>convolutional neural network</subject><subject>Image recognition</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>lensless imaging</subject><subject>Privacy</subject><subject>privacy preserving</subject><subject>Training</subject><subject>Visualization</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1Lw0AQDaJgqf0Fegh4Tt3vj2Mt1QYKHqrnZZPM1i35qLsp4r83NUU6lxnevPeGeUlyj9EcY6SfFsvlarudE0TonFKkkNBXyYRgoTPKqbi-mG-TWYx7NJQaIC4nyfP62Ng2y5sDhBIOvS9qSPMK2t47X9red2367fvPdAM2tPa03UAba4gxzRu78-3uLrlxto4wO_dp8vGyel-us83ba75cbLKSId1nXLISK-kKUQmptSIFLxEg6oRVghe2AigLVClmMXaOOKsRFhXhVNKBJzidJvnoW3V2bw7BNzb8mM568wd0YWds6H1Zg7GAoZCV4FYVjGmqyeAnNUWMcgVODl6Po9chdF9HiL3Zd8fhvzoaogSjjBF8YtGRVYYuxgDu_ypG5pS9GbM3p-zNOftB9TCqPABcKAiRRBP6C__IfyQ</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Canh, Thuong Nguyen</creator><creator>Ngo, Trung Thanh</creator><creator>Nagahara, Haijme</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Cameras Compressive Sensing convolutional neural network Image recognition Image reconstruction Imaging lensless imaging Privacy privacy preserving Training Visualization |
| title | Human-Imperceptible Identification with Learnable Lensless Imaging |
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