The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Terdapat beberapa teknologi seperti kod QR untuk mendapatkan maklumat digital daripada bahan bercetak. Penanda air digital adalah salah satu daripada teknik tersebut. Berbanding dengan teknik lain, penanda air digital sesuai untuk menambah maklumat pada imej tanpa merosakkan reka bentuknya. Untuk tujuan tersebut, kaedah penanda air digital untuk perkara bercetak menggunakan penanda pengesanan atau teknik pendaftaran imej untuk mengesan kawasan bertanda air dicadangkan. Walau bagaimanapun, penanda pengesanan itu sendiri boleh merosakkan penampilan sehingga kelebihan penanda air digital, yang tidak kehilangan reka bentuk, tidak digunakan sepenuhnya. Sebaliknya, kaedah menggunakan teknik pendaftaran imej tidak dapat berfungsi untuk imej yang tidak didaftarkan. Dalam makalah ini, kami mencadangkan kaedah penanda air digital baru menggunakan pembelajaran mendalam untuk pengesanan kawasan bertanda air dan bukannya menggunakan penanda pengesanan atau pendaftaran imej. Kaedah yang dicadangkan memperkenalkan pembahagian semantik berdasarkan model pembelajaran mendalam untuk mengesan kawasan bertanda air daripada bahan bercetak. Kami menyediakan dua set data untuk melatih model pembelajaran mendalam. Satu terdiri daripada imej tidak bertanda air dan bertanda air yang diubah secara geometri. Bilangan imej dalam set data ini agak besar kerana imej boleh dijana berdasarkan pemprosesan imej. Set data ini digunakan untuk pra-latihan. Satu lagi diperolehi daripada gambar yang sebenarnya diambil termasuk perkara bercetak tidak bertanda air atau bertanda air. Bilangan set data ini agak kecil kerana mengambil gambar memerlukan banyak usaha dan masa. Walau bagaimanapun, kewujudan pra-latihan membolehkan imej latihan yang lebih sedikit. Set data ini digunakan untuk penalaan halus untuk meningkatkan kekukuhan bagi serangan kamera cetak. Dalam eksperimen, kami menyiasat prestasi kaedah kami dengan melaksanakannya pada telefon pintar. Keputusan eksperimen menunjukkan bahawa kaedah kami boleh membawa 96 bit maklumat dengan perkara bercetak bertanda air.
Hiroyuki IMAGAWA
Osaka Prefecture University
Motoi IWATA
Osaka Prefecture University
Koichi KISE
Osaka Prefecture University
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Salinan
Hiroyuki IMAGAWA, Motoi IWATA, Koichi KISE, "Digital Watermarking Method for Printed Matters Using Deep Learning for Detecting Watermarked Areas" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 1, pp. 34-42, January 2021, doi: 10.1587/transinf.2020MUP0004.
Abstract: There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020MUP0004/_p
Salinan
@ARTICLE{e104-d_1_34,
author={Hiroyuki IMAGAWA, Motoi IWATA, Koichi KISE, },
journal={IEICE TRANSACTIONS on Information},
title={Digital Watermarking Method for Printed Matters Using Deep Learning for Detecting Watermarked Areas},
year={2021},
volume={E104-D},
number={1},
pages={34-42},
abstract={There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.},
keywords={},
doi={10.1587/transinf.2020MUP0004},
ISSN={1745-1361},
month={January},}
Salinan
TY - JOUR
TI - Digital Watermarking Method for Printed Matters Using Deep Learning for Detecting Watermarked Areas
T2 - IEICE TRANSACTIONS on Information
SP - 34
EP - 42
AU - Hiroyuki IMAGAWA
AU - Motoi IWATA
AU - Koichi KISE
PY - 2021
DO - 10.1587/transinf.2020MUP0004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2021
AB - There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.
ER -