Evolving k-Threshold Visual Cryptography Schemes
- URL: http://arxiv.org/abs/2508.15917v1
- Date: Thu, 21 Aug 2025 18:30:07 GMT
- Title: Evolving k-Threshold Visual Cryptography Schemes
- Authors: Xiaoli Zhuo, Xuehu Yan, Lintao Liu, Wei Yan,
- Abstract summary: We present a formal mathematical definition of $(k,infty)$ VCS and propose a $(k,infty)$ VCS based on random grids that works for arbitrary $k$.<n>We also develop optimized $(k,infty)$ VCS for $k=2$ and $3$, along with contrast enhancement strategies for $kgeq 4$.
- Score: 7.842676354668401
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In evolving access structures, the number of participants is countably infinite with no predetermined upper bound. While such structures have been realized in secret sharing, research in secret image sharing has primarily focused on visual cryptography schemes (VCS). However, there exists no construction for $(k,\infty)$ VCS that applies to arbitrary $k$ values without pixel expansion currently, and the contrast requires enhancement. In this paper, we first present a formal mathematical definition of $(k,\infty)$ VCS. Then, propose a $(k,\infty)$ VCS based on random grids that works for arbitrary $k$. In addition, to further improve contrast, we develop optimized $(k,\infty)$ VCS for $k=2$ and $3$, along with contrast enhancement strategies for $k\geq 4$. Theoretical analysis and experimental results demonstrate the superiority of our proposed schemes.
Related papers
- Generalized $\mathbb{Z}_p$ toric codes as qudit low-density parity-check codes [5.692499671837265]
We study two-dimensional translation-invariant CSS stabilizer codes over prime-dimensional qudits on the square lattice under twisted boundary conditions.<n>We find that the best observed $k d2$ at $n$ increases with $p$, with an empirical relation $k d2 = 0.0541, n2ln p + 3.84, n$, compatible with a Bravyi--Poulin--Terhal-type tradeoff when the interaction range grows with system size.
arXiv Detail & Related papers (2026-02-23T18:59:31Z) - $β$-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment [53.42377319350806]
$$-CLIP is a multi-granular text-conditioned contrastive learning framework.<n>$$-CAL addresses the semantic overlap inherent in this hierarchy.<n>$$-CLIP establishes a robust, adaptive baseline for dense vision-language correspondence.
arXiv Detail & Related papers (2025-12-14T13:03:20Z) - Grouped k-threshold random grid-based visual cryptography scheme [9.775517796673615]
Random grid-based VCS (RGVCS) has garnered widespread attention as it avoids pixel expansion while requiring no basic matrices design.<n> Contrast, a core metric for RGVCS, directly determines the visual quality of recovered images.<n>We propose a novel sharing paradigm for RGVCS that constructs $(k,n)$-threshold schemes from arbitrary $(k,n')$-threshold schemes.
arXiv Detail & Related papers (2025-08-07T13:44:09Z) - CODA: Repurposing Continuous VAEs for Discrete Tokenization [52.58960429582813]
textbfCODA(textbfCOntinuous-to-textbfDiscrete textbfAdaptation) is a framework that decouples compression and discretization.<n>Our approach achieves a remarkable codebook utilization of 100% and notable reconstruction FID (rFID) of $mathbf0.43$ and $mathbf1.34$ for $8 times$ and $16 times$ compression on ImageNet 256$times$ 256 benchmark.
arXiv Detail & Related papers (2025-03-22T12:59:00Z) - Optimal Computational Secret Sharing [51.599517747577266]
In $(t, n)$-threshold secret sharing, a secret $S$ is distributed among $n$ participants.<n>We present a construction achieving a share size of $tfrac|S|t + |K|t$.
arXiv Detail & Related papers (2025-02-04T23:37:16Z) - Stochastic Bandits Robust to Adversarial Attacks [33.278131584647745]
This paper investigates multi-armed bandit algorithms that are robust to adversarial attacks.
We study two cases of this model, with or without the knowledge of an attack budget $C$.
We devise two types of algorithms with regret bounds having additive or multiplicative $C$ dependence terms.
arXiv Detail & Related papers (2024-08-16T17:41:35Z) - A Construction of Evolving $k$-threshold Secret Sharing Scheme over A Polynomial Ring [55.17220687298207]
The threshold secret sharing scheme allows the dealer to distribute the share to every participant that the secret is correctly recovered from a certain amount of shares.
We propose a brand-new construction of evolving $k$-threshold secret sharing scheme for an $ell$-bit secret over a ring, with correctness and perfect security.
arXiv Detail & Related papers (2024-02-02T05:04:01Z) - On Ideal Secret-Sharing Schemes for $k$-homogeneous access structures [0.16385815610837165]
A $k$-homogeneous access structure is represented by a $k$-uniform hypergraph $mathcalH$.
In this paper, we characterize ideal $k$-homogeneous access structures using the independent sequence method.
arXiv Detail & Related papers (2023-09-14T07:37:19Z) - Extending the Design Space of Graph Neural Networks by Rethinking
Folklore Weisfeiler-Lehman [66.23316415757456]
Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years.
However, their expressive power is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test.
We propose an extension, $(k,t)$-FWL, which considers any equivariant set as neighbors instead of all nodes.
N$2$-GNN achieves record-breaking results on ZINC-Subset (0.059), outperforming previous SOTA results by 10.6%.
arXiv Detail & Related papers (2023-06-05T21:35:32Z) - Horizon-Free and Variance-Dependent Reinforcement Learning for Latent
Markov Decision Processes [62.90204655228324]
We study regret minimization for reinforcement learning (RL) in Latent Markov Decision Processes (LMDPs) with context in hindsight.
We design a novel model-based algorithmic framework which can be instantiated with both a model-optimistic and a value-optimistic solver.
arXiv Detail & Related papers (2022-10-20T21:32:01Z) - High-dimensional Asymptotics of Feature Learning: How One Gradient Step
Improves the Representation [89.21686761957383]
We study the first gradient descent step on the first-layer parameters $boldsymbolW$ in a two-layer network.
Our results demonstrate that even one step can lead to a considerable advantage over random features.
arXiv Detail & Related papers (2022-05-03T12:09:59Z) - Small Covers for Near-Zero Sets of Polynomials and Learning Latent
Variable Models [56.98280399449707]
We show that there exists an $epsilon$-cover for $S$ of cardinality $M = (k/epsilon)O_d(k1/d)$.
Building on our structural result, we obtain significantly improved learning algorithms for several fundamental high-dimensional probabilistic models hidden variables.
arXiv Detail & Related papers (2020-12-14T18:14:08Z) - Beyond Grids: Multi-objective Bayesian Optimization With Adaptive Discretization [8.674678760891528]
We propose an algorithm that exploits the smoothness of the GP-sampled function and the structure of $(cal X,d)$ to learn fast.<n>In essence, Adaptive $boldsymbolepsilon$-PAL employs a tree-based adaptive discretization technique.
arXiv Detail & Related papers (2020-06-24T21:27:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.