Signature hiding and recovery in a graph coloring solutions using modified genetic algorithm

Saha, Abhisek; Bhaumik, Debdatta; Pathak, Sanjay

doi:10.1109/iccitechn.2011.6164872

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…(2005) demonstrate a WCC bound of type (4.9) in . Various extensions of these results are given in Saha and Tewari (2011), Dekel, Eldan and Koren (2015) and Gasnikov et al. (2017).…”

Section: Methods For Convex Objectivesmentioning

confidence: 81%

Derivative-free optimization methods

2019

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Dedicated to the memory of Andrew R. Conn for his inspiring enthusiasm and his many contributions to the renaissance of derivative-free optimization methods. AbstractIn many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in these methods, with an emphasis on highlighting recent developments and on unifying treatment of such problems in the non-linear optimization and machine learning literature. We categorize methods based on assumed properties of the black-box functions, as well as features of the methods. We first overview the primary setting of deterministic methods applied to unconstrained, non-convex optimization problems where the objective function is defined by a deterministic black-box oracle. We then discuss developments in randomized methods, methods that assume some additional structure about the objective (including convexity, separability and general non-smooth compositions), methods for problems where the output of the black-box oracle is stochastic, and methods for handling different types of constraints.

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“…(2005) demonstrate a WCC bound of type (4.9) in . Various extensions of these results are given in Saha and Tewari (2011), Dekel, Eldan and Koren (2015) and Gasnikov et al. (2017).…”

Section: Methods For Convex Objectivesmentioning

confidence: 81%

Derivative-free optimization methods

2019

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“…The most common approaches to protect graph-type data are: adding new nodes [1]; inserting extra edges [2] and embedding sub-graph [3]. Since these approaches are based on the node domain, they are not robust to many attacks and not secure.…”

Section: Introductionmentioning

confidence: 99%

Graph Spectral Domain Blind Watermarking

Al-Khafaji

Abhayaratne

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model. The distortion minimisation model is proposed to reduce the watermarking distortion by establishing the relationship between the error distortion using mean square error and the selected Graph Fourier coefficients to embed the watermark. The robustness model is proposed to improve the watermarking robustness against the attacks by establishing the relationship between the watermark extraction and the effect of the attacks, namely, additive noise and nodes data deletion. The proposed models were verified by the experimental results.

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“…In this paper we are concerned with protection and authentication of such data captured via a network of sensors. However, there have been limited works on this aspect of addressing irregular graph structures.The most common approaches for protecting graph-type are: adding extra edges between a set of pairs of nodes, which have different colours based on the binary message; Maximal Independent Set (MIS) based on choosing one or more set(s) from the original graph such that the set is independent [1]; inserting new nodes to the original graph and connect them depending on the binary message [2] and hiding a sub-graph which is generated from a watermark [3]. Since these works are based on vertex domain, they are not robust to data processing, noise removal or geometrical attacks, such as, adding new nodes, edges or sub-graphs.…”

Section: Introductionmentioning

confidence: 99%

Graph spectral domain watermarking for unstructured data from sensor networks

Al-Khafaji

Abhayaratne

2017

2017 22nd International Conference on Digital Signal Processing (DSP)

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Abstract-The modern applications like social networks and sensors networks are increasingly used in the recent years. These applications can be represented as a weighted graph using irregular structure. Unfortunately, we cannot apply the techniques of the traditional signal processing on those graphs. In this paper, graph spread spectrum watermarking is proposed for networked sensor data authentication. Firstly, the graph spectrum is computed based on the eigenvector decomposition of the graph Laplacian. Then, graph Fourier coefficients are obtained by projecting the graph signals onto the basis functions which are the eigenvectors of the graph Laplacian. Finally, the watermark bits are embedded in the graph spectral coefficients using a watermark strength parameter varied according to the eigenvector number. We have considered two scenarios: blind and non-blind watermarking. The experimental results show that the proposed methods are robust, high capacity and result in low distortion in data. The proposed algorithms are robust to many types of attacks: noise, data modification, data deletion, rounding and down-sampling.

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Signature hiding and recovery in a graph coloring solutions using modified genetic algorithm

Cited by 3 publications

References 15 publications

Derivative-free optimization methods

Derivative-free optimization methods

Graph Spectral Domain Blind Watermarking

Graph spectral domain watermarking for unstructured data from sensor networks

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