Just Accepted
GSyncCode: Geometry Synchronous Hidden Code for One-step Photography Decoding
Authors: 
Chengxin Zhao, Hefei Ling, Jialie Shen, Han Fang, Yaokun Fang, Sijing Xie, Zongyi Li, Ping LiAuthors Info & Claims
Accepted on 21 November 2024
Abstract
Invisible hyperlinks and hidden barcodes have recently emerged as a hot topic in offline-to-online messaging, where an invisible message or barcode is embedded in an image and can be decoded via camera shooting. Current schemes involve a two-step decoding process: starting with vertex localization of the embedded region to correct the perspective distortion introduced by shooting, followed by decoding the message from the corrected region. However, vertex localization can be complex and time-consuming, which affects the efficiency and accuracy of message decoding. To address this issue, this paper proposes a geometry synchronous decoding scheme called GSyncCode, allowing for one-step extraction of a Data Matrix code from the photograph. Instead of correction before decoding, GSyncCode directly decodes a geometry-transformed Data Matrix that is synchronized with the embedded region. A barcode scanner is then used to efficiently retrieve messages. We design a Haar-transform based encoder HaarUNet and a HaarLoss visual function to select the key component of the Data Matrix for embedding. They improve the visual quality of the embedded image by reducing redundant embedding signals. Extensive simulated and real-world experiments demonstrate the superiority of GSyncCode in both decoding efficiency and accuracy. Our codes are published at: https://github.com/zcx-language/GSyncCode.
References
[1]
Shumeet Baluja. 2017. Hiding Images in Plain Sight: Deep Steganography. In NIPS. 2069–2079.
[2]
Shumeet Baluja. 2020. Hiding Images within Images. IEEE Trans. Pattern Anal. Mach. Intell. 42, 7 (2020), 1685–1697.
[3]
Deepayan Bhowmik and Charith Abhayaratne. 2020. Embedding Distortion Analysis in Wavelet-domain Watermarking. ACM Trans. Multim. Comput. Commun. Appl. 15, 4 (2020), 108:1–108:24.
[4]
Fang Cao, Daidou Guo, Tianjun Wang, Heng Yao, Jian Li, and Chuan Qin. 2024. Universal screen-shooting robust image watermarking with channel-attention in DCT domain. Expert Syst. Appl. 238, Part F (2024), 122062.
[5]
Qinwei Chang, Leichao Huang, Shaoteng Liu, Hualuo Liu, Tianshu Yang, and Yexin Wang. 2022. Blind Robust Video Watermarking Based on Adaptive Region Selection and Channel Reference. In ACM Multimedia. ACM, 2344–2350.
[6]
Yushi Cheng, Xiaoyu Ji, Lixu Wang, Qi Pang, Yi-Chao Chen, and Wenyuan Xu. 2021. mID: Tracing Screen Photos via Moiré Patterns. In USENIX Security Symposium. USENIX Association, 2969–2986.
[7]
Hao Cui, Huanyu Bian, Weiming Zhang, and Nenghai Yu. 2019. UnseenCode: Invisible On-screen Barcode with Image-based Extraction. In INFOCOM. IEEE, 1315–1323.
[8]
Li Dong, Jiale Chen, Chengbin Peng, Yuanman Li, and Weiwei Sun. 2022. Watermark-Preserving Keypoint Enhancement for Screen-Shooting Resilient Watermarking. In ICME. IEEE, 1–6.
[9]
Han Fang, Dongdong Chen, Qidong Huang, Jie Zhang, Zehua Ma, Weiming Zhang, and Nenghai Yu. 2021. Deep Template-Based Watermarking. IEEE Trans. Circuits Syst. Video Technol. 31, 4 (2021), 1436–1451.
[10]
Han Fang, Dongdong Chen, Feng Wang, Zehua Ma, Honggu Liu, Wenbo Zhou, Weiming Zhang, and Nenghai Yu. 2022. TERA: Screen-to-Camera Image Code With Transparency, Efficiency, Robustness and Adaptability. IEEE Trans. Multim. 24 (2022), 955–967.
[11]
Han Fang, Kejiang Chen, Yupeng Qiu, Jiayang Liu, Ke Xu, Chengfang Fang, Weiming Zhang, and Ee-Chien Chang. 2023. DeNoL: A Few-Shot-Sample-Based Decoupling Noise Layer for Cross-channel Watermarking Robustness. In ACM Multimedia. ACM, 7345–7353.
[12]
Han Fang, Zhaoyang Jia, Zehua Ma, Ee-Chien Chang, and Weiming Zhang. 2022. PIMoG: An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking Network. In ACM Multimedia. ACM, 2267–2275.
[13]
Han Fang, Weiming Zhang, Zehua Ma, Hang Zhou, Shan Sun, Hao Cui, and Nenghai Yu. 2020. A Camera Shooting Resilient Watermarking Scheme for Underpainting Documents. IEEE Trans. Circuits Syst. Video Technol. 30, 11 (2020), 4075–4089.
[14]
Han Fang, Weiming Zhang, Hang Zhou, Hao Cui, and Nenghai Yu. 2019. Screen-Shooting Resilient Watermarking. IEEE Trans. Inf. Forensics Secur. 14, 6 (2019), 1403–1418.
[15]
Zhongpai Gao, Guangtao Zhai, and Chunjia Hu. 2015. The Invisible QR Code. In ACM Multimedia. ACM, 1047–1050.
[16]
Zhenyu Guan, Junpeng Jing, Xin Deng, Mai Xu, Lai Jiang, Zhou Zhang, and Yipeng Li. 2023. DeepMIH: Deep Invertible Network for Multiple Image Hiding. IEEE Trans. Pattern Anal. Mach. Intell. 45, 1 (2023), 372–390.
[17]
Hengchang Guo, Qilong Zhang, Junwei Luo, Feng Guo, Wenbin Zhang, Xiaodong Su, and Minglei Li. 2023. Practical Deep Dispersed Watermarking with Synchronization and Fusion. In ACM Multimedia. ACM, 7922–7932.
[18]
Mingjin He, Bingwen Feng, Yizhi Guo, Jian Weng, and Wei Lu. 2024. Camera-Shooting Resilient Watermarking on Image Instance Level. IEEE Transactions on Circuits and Systems for Video Technology (2024), 1–1.
[19]
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu. 2020. Squeeze-and-Excitation Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 8 (2020), 2011–2023.
[20]
Jiangtao Huang, Ting Luo, Li Li, Gaobo Yang, Haiyong Xu, and Chin-Chen Chang. 2023. ARWGAN: Attention-Guided Robust Image Watermarking Model Based on GAN. IEEE Trans. Instrum. Meas. 72 (2023), 1–17.
[21]
Chen Hui, Shaohui Liu, Wuzhen Shi, Feng Jiang, and Debin Zhao. 2022. Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking. ACM Trans. Multim. Comput. Commun. Appl. 18, 3s (2022), 144:1–144:25.
[22]
Mark J. Huiskes and Michael S. Lew. 2008. The MIR flickr retrieval evaluation. In Multimedia Information Retrieval. ACM, 39–43.
[23]
Jun Jia, Zhongpai Gao, Kang Chen, Menghan Hu, Xiongkuo Min, Guangtao Zhai, and Xiaokang Yang. 2022. RIHOOP: Robust Invisible Hyperlinks in Offline and Online Photographs. IEEE Trans. Cybern. 52, 7 (2022), 7094–7106.
[24]
Jun Jia, Zhongpai Gao, Yiwei Yang, Wei Sun, Dandan Zhu, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai. 2024. Hidden Barcode in Sub-Images with Invisible Locating Marker. ACM Transactions on Multimedia Computing, Communications and Applications (2024).
[25]
Jun Jia, Zhongpai Gao, Dandan Zhu, Xiongkuo Min, Guangtao Zhai, and Xiaokang Yang. 2022. Learning Invisible Markers for Hidden Codes in Offline-to-online Photography. In CVPR. IEEE, 2263–2272.
[26]
Zhaoyang Jia, Han Fang, and Weiming Zhang. 2021. MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression. In ACM Multimedia. ACM, 41–49.
[27]
Junpeng Jing, Xin Deng, Mai Xu, Jianyi Wang, and Zhenyu Guan. 2021. HiNet: Deep Image Hiding by Invertible Network. In ICCV. IEEE, 4713–4722.
[28]
Xiangui Kang, Jiwu Huang, and Wenjun Zeng. 2010. Efficient general print-scanning resilient data hiding based on uniform log-polar mapping. IEEE Trans. Inf. Forensics Secur. 5, 1 (2010), 1–12.
[29]
Ladislav Karrach and Elena Pivarciová. 2021. Comparative Study of Data Matrix Codes Localization and Recognition Methods. J. Imaging 7, 9 (2021), 163.
[30]
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018. Progressive Growing of GANs for Improved Quality, Stability, and Variation. In ICLR. OpenReview.net.
[31]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR (Poster).
[32]
Yiyi Li, Xin Liao, and Xiaoshuai Wu. 2024. Screen-Shooting Resistant Watermarking with Grayscale Deviation Simulation. IEEE Transactions on Multimedia (2024), 1–16.
[33]
Peiyuan Liao, Xiuyu Li, Xihui Liu, and Kurt Keutzer. 2022. The ArtBench Dataset: Benchmarking Generative Models with Artworks. CoRR abs/2206.11404 (2022).
[34]
Jinpeng Lin, Min Zhou, Ye Ma, Yifan Gao, Chenxi Fei, Yangjian Chen, Zhang Yu, and Tiezheng Ge. 2023. AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation. In ACM Multimedia. ACM, 1250–1260.
[35]
Shao-Ping Lu, Rong Wang, Tao Zhong, and Paul L. Rosin. 2021. Large-Capacity Image Steganography Based on Invertible Neural Networks. In CVPR. Computer Vision Foundation / IEEE, 10816–10825.
[36]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In MICCAI (3) (Lecture Notes in Computer Science, Vol. 9351). Springer, 234–241.
[37]
Wuzhen Shi and Shaohui Liu. 2022. Hiding Message Using a Cycle Generative Adversarial Network. ACM Trans. Multim. Comput. Commun. Appl. 18, 3s (2022), 143:1–143:15.
[38]
Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR.
[39]
Mingxing Tan and Quoc V. Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In ICML (Proceedings of Machine Learning Research, Vol. 97). PMLR, 6105–6114.
[40]
Matthew Tancik, Ben Mildenhall, and Ren Ng. 2020. StegaStamp: Invisible Hyperlinks in Physical Photographs. In CVPR. Computer Vision Foundation / IEEE, 2114–2123.
[41]
Zongwei Tang, Xiuli Chai, Yang Lu, Binjie Wang, and Yong Tan. 2023. An end-to-end screen shooting resilient blind watermarking scheme for medical images. J. Inf. Secur. Appl. 76 (2023), 103547.
[42]
Baowei Wang, Yufeng Wu, and Guiling Wang. 2023. Adaptor: Improving the Robustness and Imperceptibility of Watermarking by the Adaptive Strength Factor. IEEE Trans. Circuits Syst. Video Technol. 33, 11 (2023), 6260–6272.
[43]
Eric Wengrowski and Kristin J. Dana. 2019. Light Field Messaging With Deep Photographic Steganography. In CVPR. Computer Vision Foundation / IEEE, 1515–1524.
[44]
Mingliang Xu, Qingfeng Li, Jianwei Niu, Hao Su, Xiting Liu, Weiwei Xu, Pei Lv, Bing Zhou, and Yi Yang. 2021. ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes. ACM Trans. Multim. Comput. Commun. Appl. 17, 1 (2021), 25:1–25:23.
[45]
Yiyan Yang, Zhongpai Gao, and Guangtao Zhai. 2021. LRS-Net: invisible QR Code embedding, detection, and restoration. In VCIP. IEEE, 1–5.
[46]
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In CVPR. Computer Vision Foundation / IEEE Computer Society, 586–595.
[47]
Chengxin Zhao, Hefei Ling, Sijing Xie, Han Fang, Yaokun Fang, and Nan Sun. 2024. SSyncOA: Self-synchronizing Object-aligned Watermarking to Resist Crop-paste Attacks. In ICME. IEEE, 1–6.
[48]
Chengxin Zhao, Hefei Ling, Sijing Xie, Nan Sun, Zongyi Li, Yuxuan Shi, and Jiazhong Chen. 2024. DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization. In IJCNN. IEEE, 1–7.
[49]
Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei. 2018. HiDDeN: Hiding Data With Deep Networks. In ECCV (15) (Lecture Notes in Computer Science, Vol. 11219). Springer, 682–697.
[50]
Liuhao Zhu, Yixiang Fang, Yi Zhao, Yi Peng, Junxiang Wang, and Jiangqun Ni. 2024. Lite Localization Network and DUE-Based Watermarking for Color Image Copyright Protection. IEEE Transactions on Circuits and Systems for Video Technology (2024), 1–1.
Index Terms
- GSyncCode: Geometry Synchronous Hidden Code for One-step Photography Decoding
Recommendations
An adaptive steganographic method based on the measurement of just noticeable distortion profile
This paper presents an adaptive steganographic method based on just noticeable distortion (JND) profile measurement. According to the input requirements, our method can produce a higher quality or higher embedding capacity stego-image. In the embedding ...
LSB steganographic method based on reversible histogram transformation function for resisting statistical steganalysis
Statistical steganalysis schemes detect the existence of secret information embedded by steganography. The x^2-detection and Regular-Singular (RS)-attack methods are two well-known statistical steganalysis schemes used against LSB (least significant bit)...
An Adaptive Steganographic Scheme for Color Images
In this paper, a new palette-based image steganography is proposed. Palette-based images such as GIF files have been widely used on the Internet and web pages such that all browsers can recognize them. Palette-based images can be used as good carriers ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
EISSN:1551-6865
Table of ContentsCopyright © 2024 Copyright held by the owner/author(s).
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Online AM: 27 November 2024
Accepted: 21 November 2024
Revised: 19 October 2024
Received: 23 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)59
- Downloads (Last 6 weeks)59
Reflects downloads up to 13 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in