A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search

Frank, Tal; Liu, Jiayin; Gat, Shani; Haik, Oren; Mor, Orel Bat; Roth, Itamar; Allebach, Jan P.; Yitzhaky, Yitzhak

doi:10.1109/tip.2022.3196821

Cited by 5 publications

(2 citation statements)

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“…Therefore, the fusion of anti-counterfeiting graphics with QR codes is gaining increasing attention. At present, the fusion can be summarized as physical unclonable function (PUF) [ 16 , 17 , 18 , 19 ], watermark [ 20 , 21 , 22 , 23 ], copy detection pattern (CDP) [ 24 , 25 , 26 , 27 , 28 ], and halftone [ 29 , 30 , 31 , 32 ]. Although the above methods can achieve certain anti-counterfeiting effects, there are still some aspects that can be improved.…”

Section: Introductionmentioning

confidence: 99%

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

Wang¹,

Zheng²,

You³

et al. 2023

Sensors

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This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

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Section: Introductionmentioning

confidence: 99%

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

Wang¹,

Zheng²,

You³

et al. 2023

Sensors

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“…Halftoning techniques aim for reproducing continuous-tone images c∈[0,1]N with binary pixels h∈{0,1}N, where N denotes the number of pixels. In addition to classic approaches like ordered dithering, 1 – 4 error diffusion, 5 – 12 and search-based methods, 13 – 18 recently, deep learning-based solutions 19 – 25 are showing their abilities in rendering decent halftones with reversibility 21 or less computational complexity 23 . Specifically, convolutional neural networks (CNNs) are trained to project white Gaussian noise maps into halftone pixels conditioning on the continuous-tone image [illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Fast halftoning policy network with error feedback

Jiang

Xiong

Yin³

et al. 2023

J. Electron. Imag.

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Deep learning-based halftoning methods have demonstrated their abilities in dithering images with desired blue-noise properties. However, tons of parameters and computations come along with the 2D convolutional neural networks (CNNs), which hinder their practical usage, particularly in the cost-sensitive printing imaging system equipped with low-end computing devices. We devise a fast halftoning policy network to alleviate the problem. Motivated by classic error diffusion algorithms, we introduce an error feedback mechanism to model the dependencies between halftone lines. Consequently, the problem collapses to 1D deep halftoning within each line, which simplifies the learning task. Furthermore, a very simple convolutional layer is applied to capture the texture details from the continuous-tone image. We find that the proposed halftoning model can stably converge under the reinforcement learning-based framework. Experiments show that our model achieves a 10.6× speedup with only 1.7% parameters compared to a previous 2D CNN-based work while maintaining similar halftone quality. We have also conducted experiments on a digital signal processor to demonstrate how the proposed model saves expensive off-chip memory accesses and improves processing throughput in real scenarios.

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A key review on graph data science: The power of graphs in scientific studies

Daş

SÜLÜ>

2023

Chemometrics and Intelligent Laboratory Systems

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A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search

Cited by 5 publications

References 50 publications

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

Fast halftoning policy network with error feedback

A key review on graph data science: The power of graphs in scientific studies

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