Randomly driven fuzzy key extraction of unclonable images

Shariati, Saloomeh; Jacques, Laurent; Standaert, François‐Xavier; Macq, Benoı̂t; Salhi, Mohamed Amin; Antoine, Philippe

doi:10.1109/icip.2010.5652420

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…This lemma is a simple consequence of Lemma 3.2 in [57] which shows that, for one measurement, In words, Lemma 2 implies that the Hamming distance between two binary measurement vectors A(x), A(s) tends to the angle between the signals x and s as the number of measurements M increases. In [57] this fact is used in the context of randomized rounding for max-cut problems; however, this property has also been used in similar contexts as ours with regards to preservation of inner products from binary measurements [58,59].…”

Section: Binary -Stable Embeddings Via Random Projectionsmentioning

confidence: 99%

See 1 more Smart Citation

Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Jacques

Laska²,

Boufounos³

et al. 2013

IEEE Trans. Inform. Theory

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575

660

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The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, there is an inverse relationship between the achievable sampling rate and the bit-depth. In this paper, we investigate an alternative CS approach that shifts the emphasis from the sampling rate to the number of bits per measurement. In particular, we explore the extreme case of 1-bit CS measurements, which capture just their sign. Our results come in two flavors. First, we consider ideal reconstruction from noiseless 1-bit measurements and provide a lower bound on the best achievable reconstruction error. We also demonstrate that i.i.d. random Gaussian matrices provide measurement mappings that, with overwhelming probability, achieve nearly optimal error decay. Next, we consider reconstruction robustness to measurement errors and noise and introduce the Binary -Stable Embedding (B SE) property, which characterizes the robustness of the measurement process to sign changes. We show that the same class of matrices that provide almost optimal noiseless performance also enable such a robust mapping. On the practical side, we introduce the Binary Iterative Hard Thresholding (BIHT) algorithm for signal reconstruction from 1-bit measurements that offers state-of-the-art performance.

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Section: Binary -Stable Embeddings Via Random Projectionsmentioning

confidence: 99%

“…This change of coordinates can be very convenient. For instance, the proof of Lemma 2 relies on the computationP[Ai(x) = Ai(s)] = µ(A = {ϕ : φD−1 ∈ [0, π dS(x, s)] ∪ [π, π + π dS(x, s)]}) = dS(x, s), since for (almost) all ϕ ∈ A,x and s live in the two different subvolumes determined by the plane {u : ϕ, u = 0}[57,58].…”

mentioning

confidence: 99%

Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Jacques

Laska²,

Boufounos³

et al. 2013

IEEE Trans. Inform. Theory

Self Cite

575

660

View full text Add to dashboard Cite

show abstract

“…Despite the tremendous improvement on the time performance, once again, this improvement may not suffice in a real-world system. Other time improvements may include parallelization of the software application and/or reducing the image to hash codes [22,55,56]. Comparing with other state-of-the-art works, Takahashi et al [12] measured a time of 0.827 s to perform 1 vs. 1 comparison, while Wigger et al [15] reported a time to perform 1 vs. 1 comparison (The authors state that: "in the present case of 115 PCB parts, the identification process for one part takes 1.11 s", so we are assuming that is the time needed to perform 115 comparisons (the identification process does not stop after finding a correct correspondence).…”

Section: Discussionmentioning

confidence: 99%

Cork as a Unique Object: Device, Method, and Evaluation

2018

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Unique Objects (UNOs) are relevant for real-world applications such as anti-counterfeiting systems. In this work, cork is demonstrated as a UNO, part of the Physical Unclonability and Disorder (PUD) system. An adequate measurement kit (illumination device) and recognition method are also devised and evaluated. Natural hills and valleys of the cork are enhanced using the illumination device and the overall robustness of the recognition application inherent to UNOs is presented. The lighting device is based on grazing light and the recognition task is based on a local feature detector and descriptor called ORB - Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The performance evaluation utilizes a private cork database (1500 photos of 500 cork stoppers) and three public iris databases. In the tests carried out on the illumination device, the results clearly show the success of capturing stable/repeatable features needed for the recognition task in the cork database. This achievement is also reflected in the perfect recognition score achieved in the cork database, in the intra-distance measure μ i n t r a , which gives the notion of average noise between measures, and in the inter-distance μ i n t e r which provides hints about the randomness/uniqueness of a cork. Regarding the recognition application, its effectiveness is further tested using the iris databases. Regardless of the fact that the recognition algorithm was not designed for the iris recognition problem, the results show that the proposed approach is capable of competing with the techniques found in the literature specially designed for iris recognition. Furthermore, the evaluation shows that the three requirements that constitute a UNO (Disorder, Operability, and Unclonability) are fulfilled, thus supporting the main assertion of this work: that cork is a UNO.

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“…The term Physically Unclonable Function was introduced by Pappu [4,5]. Since then, many di erent physical objects have been proposed as PUF candidates, including Optical PUF [4,5], Coating PUF [20], Silicon PUF [21 23], SRAM PUF [24], Paper PUF [10, 12 14], Phosphor PUF [3,15], Laser-Written PUF [16,25], etc.…”

Section: Previous Workmentioning

confidence: 99%

Security Analysis of Image-Based PUFs for Anti-counterfeiting

Shariati

Koeune

Standaert

2012

Communications and Multimedia Security

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Abstract. Physically Unclonable Functions are a promising tool to protect against counterfeiting attacks. Yet, as with any security system, it is important to embed them in a sound protocol, ensuring that no unexpected weakness is present in the mortar binding the components together. This paper proposes an anti-counterfeiting protocol that provably reduces to natural properties of its underlying components, namely an image-based Physical Function System bearing physical unclonability and an existentially unforgeable signature scheme. Experiments con rm the practical feasibility of our construction.

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Randomly driven fuzzy key extraction of unclonable images

Cited by 7 publications

References 14 publications

Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Cork as a Unique Object: Device, Method, and Evaluation

Security Analysis of Image-Based PUFs for Anti-counterfeiting

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