Salman S. Khan;Xiang Yu;Kaushik Mitra;Manmohan Chandraker;Francesco Pittaluga
{"title":"OPENCAM:无镜头光学加密摄像机","authors":"Salman S. Khan;Xiang Yu;Kaushik Mitra;Manmohan Chandraker;Francesco Pittaluga","doi":"10.1109/TCI.2024.3451953","DOIUrl":null,"url":null,"abstract":"Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless cameras - their ability to preserve the privacy of a scene by capturing encrypted measurements. However, existing lensless camera designs suffer numerous inherent privacy vulnerabilities. To demonstrate this, we develop the first comprehensive attack model for encryption cameras, and propose \n<sc>OpEnCam</small>\n– a novel lensless optical \n<bold>en</b>\ncryption \n<bold>ca</b>\nmera design that overcomes these vulnerabilities. \n<sc>OpEnCam</small>\n encrypts the incoming light before capturing it using the modulating ability of optical masks. Recovery of the original scene from an \n<sc>OpEnCam</small>\n measurement is possible only if one has access to the camera's encryption key, defined by the unique optical elements of each camera. Our \n<sc>OpEnCam</small>\n design introduces two major improvements over existing lensless camera designs - (a) the use of two co-axially located optical masks, one stuck to the sensor and the other a few millimeters above the sensor and (b) the design of mask patterns, which are derived heuristically from signal processing ideas. We show, through experiments, that \n<sc>OpEnCam</small>\n is robust against a range of attack types while still maintaining the imaging capabilities of existing lensless cameras. We validate the efficacy of \n<sc>OpEnCam</small>\n using simulated and real data. Finally, we built and tested a prototype in the lab for proof-of-concept.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1306-1316"},"PeriodicalIF":4.2000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"OpEnCam: Lensless Optical Encryption Camera\",\"authors\":\"Salman S. Khan;Xiang Yu;Kaushik Mitra;Manmohan Chandraker;Francesco Pittaluga\",\"doi\":\"10.1109/TCI.2024.3451953\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless cameras - their ability to preserve the privacy of a scene by capturing encrypted measurements. However, existing lensless camera designs suffer numerous inherent privacy vulnerabilities. To demonstrate this, we develop the first comprehensive attack model for encryption cameras, and propose \\n<sc>OpEnCam</small>\\n– a novel lensless optical \\n<bold>en</b>\\ncryption \\n<bold>ca</b>\\nmera design that overcomes these vulnerabilities. \\n<sc>OpEnCam</small>\\n encrypts the incoming light before capturing it using the modulating ability of optical masks. Recovery of the original scene from an \\n<sc>OpEnCam</small>\\n measurement is possible only if one has access to the camera's encryption key, defined by the unique optical elements of each camera. Our \\n<sc>OpEnCam</small>\\n design introduces two major improvements over existing lensless camera designs - (a) the use of two co-axially located optical masks, one stuck to the sensor and the other a few millimeters above the sensor and (b) the design of mask patterns, which are derived heuristically from signal processing ideas. We show, through experiments, that \\n<sc>OpEnCam</small>\\n is robust against a range of attack types while still maintaining the imaging capabilities of existing lensless cameras. We validate the efficacy of \\n<sc>OpEnCam</small>\\n using simulated and real data. 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Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless cameras - their ability to preserve the privacy of a scene by capturing encrypted measurements. However, existing lensless camera designs suffer numerous inherent privacy vulnerabilities. To demonstrate this, we develop the first comprehensive attack model for encryption cameras, and propose
OpEnCam
– a novel lensless optical
en
cryption
ca
mera design that overcomes these vulnerabilities.
OpEnCam
encrypts the incoming light before capturing it using the modulating ability of optical masks. Recovery of the original scene from an
OpEnCam
measurement is possible only if one has access to the camera's encryption key, defined by the unique optical elements of each camera. Our
OpEnCam
design introduces two major improvements over existing lensless camera designs - (a) the use of two co-axially located optical masks, one stuck to the sensor and the other a few millimeters above the sensor and (b) the design of mask patterns, which are derived heuristically from signal processing ideas. We show, through experiments, that
OpEnCam
is robust against a range of attack types while still maintaining the imaging capabilities of existing lensless cameras. We validate the efficacy of
OpEnCam
using simulated and real data. Finally, we built and tested a prototype in the lab for proof-of-concept.
期刊介绍:
The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.