Visual Cryptography Scheme With Meaningful Shares Based on QR Codes

Fu, Zhengxin; Cheng, Yuqiao; Yu, Bin

doi:10.1109/access.2018.2874527

Cited by 24 publications

(20 citation statements)

References 26 publications

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“…Algorithm 3 describes the corresponding recovery algorithm's steps. In the scheme of Fu et al [5], the authors reconstructed the secret image by performing the XOR operation on the pixel values of shadows, which is an approach different from our scheme.…”

Section: Recovery Phase Of the Proposed Schemementioning

confidence: 99%

“…In this section, we first describe the differences between the schemes developed by Chow et al [3], Wan et al [13] and Fu et al [5] using diagrams, analyze the characteristics of various schemes, and finally present an objective evaluation in Table 2. Figure 7 shows an experimental result of the scheme of Chow et al [3].…”

Section: Comparisons With Related Studiesmentioning

confidence: 99%

“…Figures 9l-n present the results of shadows SC 1 , SC 2 and SC 3 with 10% salt-and-pepper noise. Figure 9o displays the XOR-ed result of l, m, and n. Figure 10 shows sharing and recovery results of (2, 4)-threshold VSS using the scheme of Fu et al [5]. Figures 10a-d display QR code shadows denoted by S 1 , S 2 , S 3 , and S 4 that can be decoded by a standard QR reader.…”

Section: Comparisons With Related Studiesmentioning

confidence: 99%

“…Chow et al [2] used cryptographic keys to encrypt QR codes when embedding them into cover QR codes. Fu et al [5] proposed a VSS scheme utilizing the encoding redundancy of QR codes by using a probabilistic sharing method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

XOR-ed visual secret sharing scheme with robust and meaningful shadows based on QR codes

Tan

Yan

et al. 2019

Multimed Tools Appl

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Quick response (QR) codes are becoming increasingly popular in various areas of life due to the advantages of the error correction capacity, the ability to be scanned quickly and the capacity to contain meaningful content. The distribution of dark and light modules of a QR code looks random, but the content of a code can be decoded by a standard QR reader. Thus, a QR code is often used in combination with visual secret sharing (VSS) to generate meaningful shadows. There may be some losses in the process of distribution and preservation of the shadows. To recover secret images with high quality, it is necessary to consider the scheme's robustness. However, few studies examine robustness of VSS combined with QR codes. In this paper, we propose a robust (k, n)-threshold XOR-ed VSS (XVSS) scheme based on a QR code with the error correction ability. Compared with OR-ed VSS (OVSS), XVSS can recover the secret image losslessly, and the amount of computation needed is low. Since the standard QR encoder does not check if the padding codewords are correct during the encoding phase, we replace padding codewords by initial shadows shared from the secret image using XVSS to generate QR code shadows. As a result, the shadows can be decoded normally, and their error correction abilities are preserved. Once all the shadows have been collected, the secret image can be recovered losslessly. More importantly, if some conventional image attacks, including rotation, JPEG compression, Gaussian noise, salt-and-pepper noise, cropping, resizing, and even the addition of camera and screen noises are performed on the shadows, the secret image can still be recovered. The experimental results and comparisons demonstrate the effectiveness of our scheme.

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Section: Recovery Phase Of the Proposed Schemementioning

confidence: 99%

Section: Comparisons With Related Studiesmentioning

confidence: 99%

Section: Comparisons With Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

XOR-ed visual secret sharing scheme with robust and meaningful shadows based on QR codes

Tan

Yan

et al. 2019

Multimed Tools Appl

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“…Researchers have previously proposed the use of QR codes for various applications in computer security. For example, for storing private and public information [22], secret sharing [5], visual cryptography [9], digital watermarking [6] and so on. The methods proposed in this paper are related to zero-based watermarking.…”

Section: Related Workmentioning

confidence: 99%

Protecting the Visual Fidelity of Machine Learning Datasets Using QR Codes

Chow

Susilo

Wang

et al. 2019

Machine Learning for Cyber Security

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Machine learning is becoming increasingly popular in a variety of modern technology. However, research has demonstrated that machine learning models are vulnerable to adversarial examples in their inputs. Potential attacks include poisoning datasets by perturbing input samples to mislead a machine learning model into producing undesirable results. Such perturbations are often subtle and imperceptible from a human's perspective. This paper investigates two methods of verifying the visual fidelity of image based datasets by detecting perturbations made to the data using QR codes. In the first method, a verification string is stored for each image in a dataset. These verification strings can be used to determine whether an image in the dataset has been perturbed. In the second method, only a single verification string stored and is used to verify whether an entire dataset is intact.

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Data Hiding Based on Redundant Space of WeChat Mini Program Codes

Chen

Wang

et al. 2022

Digital Forensics and Watermarking

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Visual Cryptography Scheme With Meaningful Shares Based on QR Codes

Cited by 24 publications

References 26 publications

XOR-ed visual secret sharing scheme with robust and meaningful shadows based on QR codes

XOR-ed visual secret sharing scheme with robust and meaningful shadows based on QR codes

Protecting the Visual Fidelity of Machine Learning Datasets Using QR Codes

Data Hiding Based on Redundant Space of WeChat Mini Program Codes

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