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Reliably identifying and authenticating smartphones is critical in our daily life since they are increasingly being used to manage sensitive data such as private messages and financial data. Recent researches on hardware fingerprinting show that each smartphone, regardless of the manufacturer or make, possesses a variety of hardware fingerprints that are unique, robust, and physically unclonable. There is a growing interest in designing and implementing hardware-rooted smartphone authentication which authenticates smartphones through verifying the hardware fingerprints of their built-in sensors. Unfortunately, previous fingerprinting methods either involve large registration overhead or suffer from fingerprint forgery attacks, rendering them infeasible in authentication systems. In this paper, we propose ABC, a real-time smartphone Authentication protocol utilizing the photo-response non-uniformity (PRNU) of the Built-in Camera. In contrast to previous works that require tens of images to build reliable PRNU features for conventional cameras, we are the first to observe that one image alone can uniquely identify a smartphone due to the unique PRNU of a smartphone image sensor. This new discovery makes the use of PRNU practical for smartphone authentication. While most existing hardware fingerprints are vulnerable against forgery attacks, ABC defeats forgery attacks by verifying a smartphone’s PRNU identity through a challenge response protocol using a visible light communication channel. A user captures two time-variant QR codes and sends the two images to a server, which verifies the identity by fingerprint and image content matching. The time-variant QR codes can also defeat replay attacks. Our experiments with 16,000 images over 40 smartphones show that ABC can efficiently authenticate user devices with an error rate less than 0.5%.
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Computational Sensor Fingerprints
Analysis of imaging sensors is one of the most reliable photo forensic techniques, but it is increasingly chal- lenged by complex image processing in modern cameras. The underlying photo response non-uniformity (PRNU) is distilled into a static sensor fingerprint unique for each device. This makes it easy to estimate and spoof and limits its reliability in face of sophisticated attackers. We propose to exploit computa- tional capabilities of emerging intelligent vision sensors to design next-generation computational sensor fingerprints. Such sensors allow for running neural network inference directly on raw pixels, which enables end-to-end optimization of the entire photo acquisition and distribution pipeline. Control over fingerprint generation allows for adaptation to various requirements and threat models. In this study we provide a detailed assessment of security properties and evaluate two approaches to prevent spoofing: fingerprint generation based on local image content and adversarial training. We found that adversarial training is currently impractical, but content fingerprints deliver good per- formance in the considered cross-domain (RAW-RGB) setting and could provide robust best-effort protection against photo manip- ulation. Moreover, computational fingerprints can alleviate other limitations of PRNU, e.g., its limited reliability for dark/texture content and expensive fingerprint storage that hinders scalability. To enable this line of work, we developed a novel open-source and high-fidelity simulation environment for modeling photo acquisi- tion and distribution pipelines (https://github.com/pkorus/neural- imaging).
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- Award ID(s):
- 1909488
- PAR ID:
- 10516337
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Information Forensics and Security
- Volume:
- 17
- ISSN:
- 1556-6013
- Page Range / eLocation ID:
- 2508 to 2523
- Subject(s) / Keyword(s):
- Imaging, image forensics, image sensors, watermarking.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Reliably identifying and authenticating smart- phones is critical in our daily life since they are increasingly being used to manage sensitive data such as private messages and financial data. Recent researches on hardware fingerprinting show that each smartphone, regardless of the manufacturer or make, possesses a variety of hardware fingerprints that are unique, robust, and physically unclonable. There is a growing interest in designing and implementing hardware-rooted smart- phone authentication which authenticates smartphones through verifying the hardware fingerprints of their built-in sensors. Unfortunately, previous fingerprinting methods either involve large registration overhead or suffer from fingerprint forgery attacks, rendering them infeasible in authentication systems. In this paper, we propose ABC, a real-time smartphone Au- thentication protocol utilizing the photo-response non-uniformity (PRNU) of the Built-in Camera. In contrast to previous works that require tens of images to build reliable PRNU features for conventional cameras, we are the first to observe that one image alone can uniquely identify a smartphone due to the unique PRNU of a smartphone image sensor. This new discovery makes the use of PRNU practical for smartphone authentication. While most existing hardware fingerprints are vulnerable against forgery attacks, ABC defeats forgery attacks by verifying a smartphone’s PRNU identity through a challenge response protocol using a visible light communication channel. A user captures two time-variant QR codes and sends the two images to a server, which verifies the identity by fingerprint and image content matching. The time-variant QR codes can also defeat replay attacks. Our experiments with 16,000 images over 40 smartphones show that ABC can efficiently authenticate user devices with an error rate less than 0.5%.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TIFS.2022.3179945
