scholarly journals Perceptual hashing method for video content authentication with maximized robustness

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qiang Ma ◽  
Ling Xing
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Perceptual Content ◽  
Video Content ◽  
Content Authentication ◽  
Constrained Optimization Problem ◽  
Video Hashing ◽  
Perceptual Hashing ◽  
Video Content Authentication ◽  
Perceptual Video Hashing

AbstractPerceptual video hashing represents video perceptual content by compact hash. The binary hash is sensitive to content distortion manipulations, but robust to perceptual content preserving operations. Currently, boundary between sensitivity and robustness is often ambiguous and it is decided by an empirically defined threshold. This may result in large false positive rates when received video is to be judged similar or dissimilar in some circumstances, e.g., video content authentication. In this paper, we propose a novel perceptual hashing method for video content authentication based on maximized robustness. The developed idea of maximized robustness means that robustness is maximized on condition that security requirement of hash is first met. We formulate the video hashing as a constrained optimization problem, in which coefficients of features offset and robustness are to be learned. Then we adopt a stochastic optimization method to solve the optimization. Experimental results show that the proposed hashing is quite suitable for video content authentication in terms of security and robustness.

scholarly journals Learning Compact Model for Large-Scale Multi-Label Data

2019 ◽  
Vol 33 ◽  
pp. 5385-5392 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Performance Metrics ◽  
State Of The Art ◽  
Optimization Method ◽  
Model Parameters ◽  
Generalization Capability ◽  
Constrained Optimization Problem ◽  
Unseen Data ◽  
Model Size

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.

scholarly journals Multi-Objective Optimization of Gas Pipeline Networks

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5141
Author(s):  
Andrzej J. Osiadacz ◽  
Niccolo Isoli
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Nonlinear Constrained Optimization ◽  
Scalar Optimization ◽  
Gas Networks ◽  
Pipeline Networks ◽  
Gas Supply

The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.

scholarly journals On a New Optimization Method With Constraints

2020 ◽  
Vol 12 (5) ◽  
pp. 27
Author(s):  
Bouchta x RHANIZAR
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Convex Set ◽  
Optimization Method ◽  
New Method ◽  
Constrained Optimization Problem ◽  
Numerical Examples ◽  
Unconstrained Problem ◽  
Closed Convex Set

We consider the constrained optimization problem  defined by: $$f(x^*) = \min_{x \in  X} f(x) \eqno (1)$$ where the function  $f$ : $ \pmb{\mathbb{R}}^{n} \longrightarrow \pmb{\mathbb{R}}$ is convex  on a closed convex set X. In this work, we will give a new method to solve problem (1) without bringing it back to an unconstrained problem. We study the convergence of this new method and give numerical examples.

scholarly journals Intellectualization of a method for solving a logistics problem to optimize costs within the framework of Lean Production technology

2020 ◽  
Vol 1 (3) ◽  
pp. 7-17
Author(s):  
Eugene Fedorov ◽  
Peter Nikolyuk ◽  
Olga Nechporenko ◽  
Esta Chioma
Keyword(s):  
Computational Complexity ◽  
T Cell ◽  
Production Technology ◽  
Optimization Problem ◽  
Cell Model ◽  
Lean Production ◽  
Optimization Method ◽  
Metaheuristic Algorithm ◽  
Constrained Optimization Problem ◽  
Real Potential

In the article, within the framework of intellectualization of the Lean Production technology, it is proposed to optimize the costs arising from the insufficient efficiency of placing goods in the warehouse by creating an optimization method based on the immune metaheuristics of the T-cell model, which allows solving the knapsack constrained optimization problem. The proposed metaheuristic method does not require specifying the probability of mutation, the number of mutations, the number of selected new cells and allows using only binary potential solutions, which makes discrete optimization possible and reduces computational complexity by preventing permanent transformations of real potential solutions into intermediate binary ones and vice versa. An immune metaheuristic algorithm based on the T-cell model has been created, intended for implementation on the GPU using the CUDA parallel information processing technology. The proposed optimization method based on immune metaheuristics can be used to intellectualize the Lean Production technology. The prospects for further researches are to test the proposed methods on a wider set of test databases.

scholarly journals New Authentication Method for Vector Geographic Data Based on Perceptual Hash

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
LiMing Zhang ◽  
XinGang Zhang
Keyword(s):  
Original Data ◽  
Multimedia Data ◽  
Perceptual Content ◽  
Geometric Transformation ◽  
Compact Representation ◽  
Content Authentication ◽  
Data Authentication ◽  
Geographic Data ◽  
Perceptual Hash ◽  
Perceptual Hashing

<p><strong>Abstract.</strong> In the Internet era, vector data can conveniently be stored, distributed and disseminated. This make it convenience for people to use them, and also make it easier for people to edit or modify them, which can reduce the credibility of Geo-information. Traditionally, authentication is done by using a hash function to generate a digital signature for data authentication. However, this method is very sensitive for change even one bit and it is appropriate for accurate authentication such as text. For geographic data, it may experience lossy compression, filtering distortion, geometric transformation, noise pollution, etc. during transmission and application, but it is consistent with the original data perception. Therefore, the traditional cryptography authentication method is not applicable to the robust authentication of geographic data.</p><p>Among various authentication techniques, perceptual hashing is a promising solution. Perceptual hashing is a type of unidirectional mapping of multimedia data sets to perceptual digest sets, that is, a multimedia digital representation with the same perceptual content is uniquely mapped into a piece of digital digest, and satisfies perceptual robustness and security. Since a perceptual hash value is a compact representation of the original content, it can be used for robust content authentication of vector geographic data. The advantage of perceptual hashing algorithms over traditional cryptographic hashing algorithms is that they can tolerate differences in quality and format. The same content is always mapped to the same hash value. This is very effective for robust authentication of geographic data.</p><p>In this work, we focus on vector geographic data content authentication by perceptual hash algorithms. In the research of vector geographic data authentication, there are usually some authentication methods based on statistics, rough representation of images and extraction of mutation points based on wavelet transform. However, these methods are more or less sensitive to geometric transformation, poor anti-aggression, high complexity, and poor robustness. In order to avoid the shortcomings of traditional authentication algorithms, this paper proposes a vector geographic data authentication algorithm combining DCT and perceptual hashing. The algorithm has high robustness and security against attack, translation, rotation and two types of collusion attacks.</p>

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