Grouped k-threshold random grid-based visual cryptography scheme
- URL: http://arxiv.org/abs/2508.05394v1
- Date: Thu, 07 Aug 2025 13:44:09 GMT
- Title: Grouped k-threshold random grid-based visual cryptography scheme
- Authors: Xiaoli Zhuo, Xuehu Yan, Wei Yan,
- Abstract summary: 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.
- Score: 9.775517796673615
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Visual cryptography schemes (VCSs) belong to a category of secret image sharing schemes that do not require cryptographic knowledge for decryption, instead relying directly on the human visual system. Among VCSs, random grid-based VCS (RGVCS) has garnered widespread attention as it avoids pixel expansion while requiring no basic matrices design. Contrast, a core metric for RGVCS, directly determines the visual quality of recovered images, rendering its optimization a critical research objective. However, existing $(k,n)$ RGVCSs still fail to attain theoretical upper bounds on contrast, highlighting the urgent need for higher-contrast constructions. In this paper, we propose a novel sharing paradigm for RGVCS that constructs $(k,n)$-threshold schemes from arbitrary $(k,n')$-threshold schemes $(k \leq n'\leq n)$, termed \emph{$n'$-grouped $(k,n)$ RGVCS}. This paradigm establishes hierarchical contrast characteristics: participants within the same group achieve optimal recovery quality, while inter-group recovery shows a hierarchical contrast. We further introduce a new contrast calculation formula tailored to the new paradigm. Then, we propose a contrast-enhanced $(k,n)$ RGVCS by setting $n'= k$, achieving the highest contrast value documented in the existing literature. Theoretical analysis and experimental results demonstrate the superiority of our proposed scheme in terms of contrast.
Related papers
- Non-Contrastive Vision-Language Learning with Predictive Embedding Alignment [12.336161969869567]
We introduce NOVA, a NOn-contrastive Vision-language Alignment framework based on joint embedding prediction with distributional regularization.<n>We evaluate NOVA on zeroshot chest X-ray classification using ClinicalBERT as the text encoder and Vision Transformers trained from scratch on MIMIC-CXR.<n>Our results demonstrate that non-contrastive vision-language pretraining offers a simpler, more stable, and more effective alternative to contrastive methods.
arXiv Detail & Related papers (2026-01-31T10:57:46Z) - ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation [64.84095852784714]
Residual Tokenizer (ResTok) is a 1D visual tokenizer that builds hierarchical residuals for both image tokens and latent tokens.<n>We show that restoring hierarchical residual priors in visual tokenization significantly improves AR image generation, achieving a gFID of 2.34 on ImageNet-256 with only 9 sampling steps.
arXiv Detail & Related papers (2026-01-07T14:09:18Z) - $β$-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) - Group Critical-token Policy Optimization for Autoregressive Image Generation [32.472222192052044]
Key obstacle lies in how to identify more critical image tokens during AR generation and implement effective token-wise optimization for them.<n>We identify the critical tokens in RLVR-based AR generation from three perspectives, specifically: $textbf(1)$ Causal dependency: early tokens fundamentally determine the later tokens and final image effect due to unidirectional dependency; $textbf(2)$ Entropy-induced spatial structure: tokens with high entropy gradients correspond to image structure and bridges distinct visual regions.<n>Experiments on multiple text-to-image benchmarks for both AR models and unified multimodal models demonstrate the effectiveness
arXiv Detail & Related papers (2025-09-26T15:33:18Z) - Evolving k-Threshold Visual Cryptography Schemes [7.842676354668401]
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 $kgeq 4$.
arXiv Detail & Related papers (2025-08-21T18:30:07Z) - End-to-End Vision Tokenizer Tuning [73.3065542220568]
The vision tokenizer optimized for low-level reconstruction is to downstream tasks requiring varied representations and semantics.<n>The loss of the vision tokenization can be the representation bottleneck for target tasks.<n>We propose ETT, an end-to-end vision tokenizer tuning approach that enables joint optimization between vision tokenization and target autoregressive tasks.
arXiv Detail & Related papers (2025-05-15T17:59:39Z) - Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion [19.46433323866636]
Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance.<n>We present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel $k$-means.<n>We propose a low-pass graph filtering strategy within a multiple linear $k$-means framework to mitigate noise and redundancy.
arXiv Detail & Related papers (2024-12-24T06:24:08Z) - Pruning is Optimal for Learning Sparse Features in High-Dimensions [15.967123173054535]
We show that a class of statistical models can be optimally learned using pruned neural networks trained with gradient descent.
We show that pruning neural networks proportional to the sparsity level of $boldsymbolV$ improves their sample complexity compared to unpruned networks.
arXiv Detail & Related papers (2024-06-12T21:43:12Z) - Image contrast enhancement based on the Schrödinger operator spectrum [0.276240219662896]
We propose a novel image contrast enhancement method based on projecting images onto the squared eigenfunctions of the two-dimensional Schr"odinger operator.
This projection relies on a design parameter, $gamma$, which controls pixel intensity during image reconstruction.
Results demonstrate that the proposed method effectively enhances image contrast while preserving the inherent characteristics of the original image.
arXiv Detail & Related papers (2024-06-04T12:37:11Z) - Expressive Losses for Verified Robustness via Convex Combinations [67.54357965665676]
We study the relationship between the over-approximation coefficient and performance profiles across different expressive losses.
We show that, while expressivity is essential, better approximations of the worst-case loss are not necessarily linked to superior robustness-accuracy trade-offs.
arXiv Detail & Related papers (2023-05-23T12:20:29Z) - Learning Low-Rank Representations for Model Compression [6.721845345130468]
We propose a Low-Rank Representation Vector Quantization ($textLR2textVQ$) method that outperforms previous VQ algorithms in various tasks and architectures.
In our method, the compression ratio could be directly controlled by $m$, and the final accuracy is solely determined by $tilded$.
With a proper $tilded$, we evaluate $textLR2textVQ$ with ResNet-18/ResNet-50 on ImageNet classification datasets, achieving 2.8%/1.0% top
arXiv Detail & Related papers (2022-11-21T12:15:28Z) - Nonconvex ${{L_ {{1/2}}}} $-Regularized Nonlocal Self-similarity
Denoiser for Compressive Sensing based CT Reconstruction [0.0]
Recently, the nonL1/2 $-norm has achieved promising performance in recovery, while the applications are unsatisfactory due to its nonsimilarity.
In this paper, we develop a CT reconstruction problem which is sparse on $_ $ $ minimization.
Extensive results on typical images have demonstrated our approach to achieve better performance.
arXiv Detail & Related papers (2022-05-15T05:24:48Z) - Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased
Scene Graph Generation [62.96628432641806]
Scene Graph Generation aims to first encode the visual contents within the given image and then parse them into a compact summary graph.
We first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction.
We then devise an innovative Group Collaborative Learning strategy to optimize the decoder.
arXiv Detail & Related papers (2022-03-18T09:14:13Z) - Bi-level Feature Alignment for Versatile Image Translation and
Manipulation [88.5915443957795]
Generative adversarial networks (GANs) have achieved great success in image translation and manipulation.
High-fidelity image generation with faithful style control remains a grand challenge in computer vision.
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance.
arXiv Detail & Related papers (2021-07-07T05:26:29Z) - Adversarial Linear Contextual Bandits with Graph-Structured Side
Observations [80.95090605985042]
A learning agent repeatedly chooses from a set of $K$ actions after being presented with a $d$-dimensional context vector.
The agent incurs and observes the loss of the chosen action, but also observes the losses of its neighboring actions in the observation structures.
Two efficient algorithms are developed based on textttEXP3.
arXiv Detail & Related papers (2020-12-10T15:40:07Z) - The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors.
We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS.
To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
arXiv Detail & Related papers (2020-05-16T08:17:44Z) - GeoDA: a geometric framework for black-box adversarial attacks [79.52980486689287]
We propose a framework to generate adversarial examples in one of the most challenging black-box settings.
Our framework is based on the observation that the decision boundary of deep networks usually has a small mean curvature in the vicinity of data samples.
arXiv Detail & Related papers (2020-03-13T20:03:01Z) - Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological
Reconstruction and Wavelet frames [146.63177174491082]
Fuzzy $C$-Means (FCM) algorithm incorporates a morphological reconstruction operation and a tight wavelet frame transform.
We present an improved FCM algorithm by imposing an $ell_0$ regularization term on the residual between the feature set and its ideal value.
Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
arXiv Detail & Related papers (2020-02-14T10:00:03Z)
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.