Yanxiao Liu

Bio: Yanxiao Liu is an academic researcher from Xidian University. The author has contributed to research in topics: Image sharing & Secret sharing. The author has an hindex of 12, co-authored 24 publications receiving 391 citations.

Enhanced embedding capacity for the SMSD-based data-hiding method

Abstract: The embedding capacity and stego-image quality are two important features in data-hiding schemes. In 2016, Kuo et al. introduced sparse modified signed-digit (SMSD)-based data hiding that could embed secret data into a group of n cover-pixels. Compared with previous exploiting modification direction (EMD) and generalized EMD (GEMD)-based data hiding, SMSD data hiding has higher embedding capacity and better stego-image quality. In this paper, we introduce a new definition of the enhanced MSD (EMSD) representation and propose an EMSD-based data-hiding scheme that improves the embedding capacity of SMSD data hiding and maintains equal quality of the stego-image. In addition, the embedding capacity in the proposed scheme is further improved using the section-wise approach in EMSD data hiding.

Threshold changeable secret image sharing scheme based on interpolation polynomial

Abstract: In previous (k,n) secret image sharing scheme, the threshold k is decided by dealer according to the security requirement, and this threshold value is fixed without considering the dynamic secure environment in future. In this work, we propose a novel threshold changeable secret image sharing scheme where the threshold value can be changed according to the changeable security requirement. In our scheme, each participant only needs to keep one initial shadow. When reconstructing image, the dealer decides the threshold according to security level. If the threshold is unchanged, any k or more initial shadows can recover the image; else if the threshold is increased or decreased, dealer publishes additional information, each participant update their shadows accordingly such that the threshold of updated shadows is changed correspondingly. The contribution of our work is that the threshold of shadows can be changed flexibly to satisfy the dynamic secure environment, and each participant only need to keep one initial shadows. The feature of threshold changeable makes our scheme more practical than previous secret image sharing in some complicated applications.

Thresholds Based Image Extraction Schemes in Big Data Environment in Intelligent Traffic Management

Abstract: Video traffic monitoring is an inexpensive and convenient source of traffic data. Traffic images processing are widely used to check traffic conditions and they can determine traffic control strategies in intelligent transportation systems (ITS). However, these traffic images always contain privacy-related data, such as vehicles registration numbers, human faces. Misuse of such data is a threat to the privacy of vehicles divers, passengers, pedestrians, etc. This paper proposes a thresholds-based images extraction solution for ITS. At first, a Faster Region Convolutional Neural Networks (RCNN) model is used to segment a traffic image into multi-regions with different importance levels; then, multi-threshold image extraction schemes are designed based on progressive secret image sharing schemes to extract images contain key traffic information, such as reg number, human faces, in which the region with higher importance level requires higher threshold for extraction. For different roles in ITS, they can extract images with different details, which can protect privacy and anonymity. The proposed methods provide a safe and intelligent way to extract images that can be used for further analysis in ITS.

Scalable secret image sharing scheme with essential shadows

Abstract: In scalable ( k , n ) secret image sharing schemes, original secret image can be partially recovered from any set of k shadows gradually, the entire image can be recovered from n shadows. Recently, ( t , s , k , n ) secret image sharing with essential shadows were proposed where the n shadows are divided into s essential shadows and n − s normal shadows, secret image can be reconstructed only if there are k or more shadows which include at least t essential shadows. Both the scalable secret image sharing and secret image sharing with essential shadows are practical and attract adequate focus in recent years. In this paper, we propose a new scalable ( t , s , k , n ) secret image sharing scheme with essential shadows. In our scheme, k or more shadows which include at least t essential shadows can gradually reconstruct secret image, entire secret image can be reconstructed when all s essential shadows are involved. Our scheme combines both features of scalable secret image sharing and secret image sharing with essential shadows, which is reasonable and practical in many applications. The size of shadows in proposed scheme is efficient comparing with previous ( t , s , k , n ) secret image sharing schemes with essential shadows.

Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts

Abstract: A recent set of overused population-based methods have been published in recent years. Despite their popularity, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar immature exploration and exploitation components and operations, and an insecure tradeoff between exploration and exploitation trends in most of the new real-world cases. Therefore, all users need to extensively modify and adjust their operations based on main evolutionary methods to reach faster convergence, more stable balance, and high-quality results. To move the optimization community one step ahead toward more focus on performance rather than change of metaphor, a general-purpose population-based optimization technique called Hunger Games Search (HGS) is proposed in this research with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. The proposed HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. The effectiveness of HGS was verified by comparing HGS with a comprehensive set of popular and advanced algorithms on 23 well-known optimization functions and the IEEE CEC 2014 benchmark test suite. Also, the HGS was applied to several engineering problems to demonstrate its applicability. The results validate the effectiveness of the proposed optimizer compared to popular essential optimizers, several advanced variants of the existing methods, and several CEC winners and powerful differential evolution (DE)-based methods abbreviated as LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods in handling many single-objective problems. We designed this open-source population-based method to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity. The method is very flexible and scalable to be extended to fit more form of optimization cases in both structural aspects and application sides. This paper's source codes, supplementary files, Latex and office source files, sources of plots, a brief version and pseudocode, and an open-source software toolkit for solving optimization problems with Hunger Games Search and online web service for any question, feedback, suggestion, and idea on HGS algorithm will be available to the public at https://aliasgharheidari.com/HGS.html .

When an attacker meets a cipher-image in 2018: A year in review

Abstract: This paper aims to review the encountered technical contradictions when an attacker meets the cipher-images encrypted by the image encryption schemes (algorithms) proposed in 2018 from the viewpoint of an image cryptanalyst. The most representative works among them are selected and classified according to their essential structures. Almost all image cryptanalysis works published in 2018 are surveyed due to their small number. The challenging problems on design and analysis of image encryption schemes are summarized to receive the attentions of both designers and attackers (cryptanalysts) of image encryption schemes, which may promote solving scenario-oriented image security problems with new technologies.

Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance

Abstract: Whale Optimization Algorithm (WOA) is a popular swarm-based algorithm with some spotted defects in its generated patterns during the searching phases. In this study, an enhanced WOA-based method is proposed in order to overcome the drawbacks of slow convergence speed and easy falling of WOA into the local optimum. The designed variant is called enhanced WOA (EWOA), which combines two strategies at the same time. First, a new communication mechanism (CM) is embedded into the basic WOA to promote the global optimal search ability and the exploitation tendency of the WOA. Then, the Biogeography-based Optimization (BBO) algorithm is partially utilized to harmonize the exploration and exploitation trends. A representative set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed EWOA. The experimental results show that the exploration ability, exploitation ability, state of the balance, and convergence style of the algorithm has been improved significantly. Based on results, the proposed EWOA is a promising and excellent algorithm, and it has achieved better solution quality and faster convergence rate compared with other most advanced algorithms. For access to material and guide for users of this paper, we host an online page at https://aliasgharheidari.com .