Image denoising in the encrypted domain

Pedrouzo-Ulloa, Alberto; Troncoso-Pastoriza, Juan Ramón; Pérez-González, Fernando

doi:10.1109/wifs.2016.7823916

Cited by 21 publications

(17 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [5], we proposed a fully unattended solution, based on RLWE (Ring Learning with Errors) cryptosystems [6], which is able to homomorphically evaluate the PRNU extraction/detection without the need of the intervention of the secret key owner in the middle of the process. To this aim, we resort to the pre-/post processing introduced in [7] and the homomorphic Wavelet-denoising proposed in [8].…”

Section: Our Fully Unattended Solutionmentioning

confidence: 99%

Efficient PRNU Matching in the Encrypted Domain

Pedrouzo-Ulloa

Masciopinto

Troncoso-Pastoriza

et al. 2019

The 2nd XoveTIC Conference (XoveTIC 2019)

Self Cite

View full text Add to dashboard Cite

Photoresponse Non-Uniformity (PRNU) is becoming particularly relevant within digital media forensics, as a means to effectively determine the source camera of a given image. Most of the practical applications in digital media forensics involve dealing with highly sensitive data whose content must be protected. In this context, several secure frameworks have been proposed to perform PRNU-based camera attribution while preserving the privacy of both the testing images and the PRNU fingerprint. The two most recent and relevant ones, independently proposed in 2018, are (a) Mohanty et al.’s, who combine the use of a trusted environment (ARM TrustZone) to compute the PRNU fingerprint, with the Boneh-Goh-Nissim (BGN) cryptosystem to perform the matching, and (b) Pedrouzo-Ulloa et al.’s, who propose a more flexible solution which can be fully implemented on a general purpose architecture and does not require access to a trusted environment. In this work, we revisit the existing frameworks and propose a general formulation for PRNU matching based on lattice cryptosystems which improves on the BGN-based solution in terms of efficiency, flexibility and privacy.

show abstract

Section: Our Fully Unattended Solutionmentioning

confidence: 99%

Efficient PRNU Matching in the Encrypted Domain

Pedrouzo-Ulloa

Masciopinto

Troncoso-Pastoriza

et al. 2019

The 2nd XoveTIC Conference (XoveTIC 2019)

Self Cite

View full text Add to dashboard Cite

show abstract

“…They utilized the additive and scalar-multiplicative homomorphisms of Shamir's SSS for basic filtering and thresholding operations that occur during the denoising process. Alberto et al Pedrouzo-Ulloa et al (2016) presented a secure image denoising model using ring learning with errors, a lattice-based cryptosystem, which is fully non-interactive in comparison to SaghaianNejadEsfahani et al (2012). Hsu et al Hsu et al (2012) proposed a model for extracting SIFT features in ED using the Paillier cryptosystem.…”

Section: Privacy-preserving Image Processingmentioning

confidence: 99%

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

View full text Add to dashboard Cite

<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

show abstract

“…The majority of the traditional SSP approaches make use of additive cryptosystems like Paillier [1], which enables the calculation of additions between encrypted values by multiplying their encryptions; however, additive homomorphisms lack flexibility for tackling more complex and non-linear operations. Hence, the use of lattice cryptosystems which present a ring homomorphism (addition and multiplication) is being progressively adopted by researchers in the field [3], [4], [5], [6]; an example is Lauter's cryptosystem [7], a Somewhat Homomorphic Encryption (SHE) based on the Ring Learning with Errors (RLWE) problem that can evaluate a bounded number of consecutive encrypted operations. Other recent representative RLWE-based examples are FV [8] and YASHE [9], cryptosystems that outperform Lauter's in terms of both efficiency and the upper bound on the number of encrypted operations.…”

Section: Preliminariesmentioning

confidence: 99%

Number Theoretic Transforms for Secure Signal Processing

Pedrouzo-Ulloa

Troncoso-Pastoriza

Pérez-González

2017

IEEE Trans.Inform.Forensic Secur.

Self Cite

View full text Add to dashboard Cite

Multimedia contents are inherently sensitive signals that must be protected whenever they are outsourced to an untrusted environment. This problem becomes a challenge when the untrusted environment must perform some processing on the sensitive signals; a paradigmatic example is Cloud-based signal processing services. Approaches based on Secure Signal Processing (SSP) address this challenge by proposing novel mechanisms for signal processing in the encrypted domain and interactive secure protocols to achieve the goal of protecting signals without disclosing the sensitive information they convey.This work presents a novel and comprehensive set of approaches and primitives to efficiently process signals in an encrypted form, by using Number Theoretic Transforms (NTTs) in innovative ways. This usage of NTTs paired with appropriate signal pre-and post-coding enables a whole range of easily composable signal processing operations comprising, among others, filtering, generalized convolutions, matrix-based processing or error correcting codes. Our main focus is on unattended processing, in which no interaction from the client is needed; for implementation purposes, efficient lattice-based somewhat homomorphic cryptosystems are used. We exemplify these approaches and evaluate their performance and accuracy, proving that the proposed framework opens up a wide variety of new applications for secured outsourced-processing of multimedia contents.

show abstract

Image denoising in the encrypted domain

Cited by 21 publications

References 13 publications

Efficient PRNU Matching in the Encrypted Domain

Efficient PRNU Matching in the Encrypted Domain

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Number Theoretic Transforms for Secure Signal Processing

Contact Info

Product

Resources

About