Using Physical Unclonable Functions for Internet-of-Thing Security Cameras

Arjona, Rosario; Prada-Delgado, Miguel A.; Arcenegui, Javier; Baturone, I.

doi:10.1007/978-3-319-93797-7_16

Cited by 2 publications

(4 citation statements)

References 18 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since some camera models include their own SRAM, the SRAM was used to generate a PUF for IoT security cameras in [ 24 ]. Six different types of memory-based PUFs, i.e., SRAM PUF, Latch PUF, D flip-flop PUF, Buskeeper PUF, and two delay-based PUFs, Arbiter PUF and a Ring Oscillator PUF, are analyzed in [ 25 ] using 192 chips with 65 nm low-power CMOS sensors.…”

Section: Related Workmentioning

confidence: 99%

Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images

Berdich

Groza

2022

Entropy

View full text Add to dashboard Cite

Camera sensor identification can have numerous forensics and authentication applications. In this work, we follow an identification methodology for smartphone camera sensors using properties of the Dark Signal Nonuniformity (DSNU) in the collected images. This requires taking dark pictures, which the users can easily do by keeping the phone against their palm, and has already been proposed by various works. From such pictures, we extract low and mid frequency AC coefficients from the DCT (Discrete Cosine Transform) and classify the data with the help of machine learning techniques. Traditional algorithms such as KNN (K-Nearest Neighbor) give reasonable results in the classification, but we obtain the best results with a wide neural network, which, despite its simplicity, surpassed even a more complex network architecture that we tried. Our analysis showed that the blue channel provided the best separation, which is in contrast to previous works that have recommended the green channel for its higher encoding power.

show abstract

Section: Related Workmentioning

confidence: 99%

Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images

Berdich

Groza

2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Many techniques have been proposed for camera identification in the last two decades [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. An identifier is extracted and associated with a camera as a fingerprint that makes it distinguishable from another one (of the same brand or even of the same model).…”

Section: Introductionmentioning

confidence: 99%

“…In the latter case, Physically Unclonable Functions (PUFs) have been reported to generate unique, distinctive and unpredictable identifiers produced by the manufacturing process variability of the hardware [ 28 ]. In contrast to metadata (which are externally assigned to the camera), or identifiers extracted from the captures (which depend on the type of external scenes), hardware-based PUFs are intrinsic to the camera and are directly extracted from the camera hardware [ 23 , 24 , 25 , 26 , 27 ]. These PUFs allow adding security functionalities at a low cost, without burdening the processing unit of the camera.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

Arjona

Prada-Delgado

Arcenegui

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.

show abstract

Using Physical Unclonable Functions for Internet-of-Thing Security Cameras

Cited by 2 publications

References 18 publications

Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images

Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

Contact Info

Product

Resources

About