Robust Deep Image Watermarking: A Survey
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Abstract
In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Steganography and Watermarking Techniques · Advanced Data Compression Techniques · Chaos-based Image/Signal Encryption
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (63)
Show source-provided references
1. Fixing the double agent vulnerability of deep watermarking: A patch-level solution against artwork plagiarism10.1109/TCSVT.2023.3295895 ↗
2. Recent trends in image watermarking techniques for copyright protection: A survey10.1007/s13735-020-00197-9 ↗
3. X. H. Jiang, “Digital watermarking and its application in image copyright protection,” presented at 2010 Int. Conf. on Intell. Comput. Techn. Autom., Changsha, China, Autom, May 11–12, 2010.
4. Robust digital watermarking techniques for copyright protection of digital data: A survey10.1007/s11277-021-08177-w ↗
5. A. Piva, M. Barni, F. Bartolini, and V. Cappellini, “DCT-based watermark recovering without resorting to the uncorrupted original image,” presented at 1997 Int. Conf. on Img. Pro., Santa Barbara, CA, USA, Oct. 26–29, 1997, pp. 26–29.
6. Digital watermarking10.1117/1.1494075 ↗
7. J. S. Mei, S. K. Li, and X. M. Tan, “A digital watermarking algorithm based on DCT and DWT,” presented at 2009 6th Web Inf. Syst. App. Conf., Xuzhou, China, Sep. 18–20, 2009.
8. Finding robust domain from attacks: A learning framework for blind watermarking10.1016/j.neucom.2019.01.067 ↗
9. Y. Luo, T. Zhou, F. Liu, and Z. Cai, “IRWArt: Levering watermarking performance for protecting high-quality artwork images,” presented at ACM Web Conf. 2023, Austin, TX, USA, Apr. 30–May 4, 2023.10.1145/3543507.3583489 ↗
10. A dual model watermarking framework for copyright protection in image processing networks
11. An automated and robust image watermarking scheme based on deep neural networks10.1109/TMM.2020.3006415 ↗
12. J. Zhu, R. Kaplan, J. Johnson, and L. Fei-Fei, “HiDDeN: Hiding data with deep networks,” presented at 15th Eur. Conf. Comput. Vis., Munich, Germany, Sep. 8–14, 2018, pp. 8–14.
13. Invisible steganography via generative adversarial networks10.1007/s11042-018-6951-z ↗
14. Y. Liu, M. Guo, J. Zhang, Y. Zhu, and X. Xie, “A novel two-stage separable deep learning framework for practical blind watermarking,” presented at 27th ACM Int. Conf. Multimed., Nice, France, Oct. 21–25, 2019.10.1145/3343031.3351025 ↗
15. X. Luo, R. Zhan, H. Chang, F. Yang, and P. Milanfar, “Distortion agnostic deep watermarking,” presented at 2020 IEEE Conf. Comput. Vis. Pattern Recognit., Seattle, WA, USA, Jun. 14–19, 2020.10.1109/CVPR42600.2020.01356 ↗
16. C. Yu, “Attention based data hiding with generative adversarial networks,” presented at 34th AAAI Conf. Artif. Intell., New York, NY, USA, Feb. 7–12, 2020.10.1609/aaai.v34i01.5463 ↗
17. RIHOOP: Robust invisible hyperlinks in offline and online photographs10.1109/TCYB.2020.3037208 ↗
18. ReDMark: Framework for residual diffusion watermarking based on deep networks10.1016/j.eswa.2019.113157 ↗
19. Digital watermarking via inverse gradient attention
20. C. Zhang, P. Benz, A. Karjauv, G. Sun, and I. S. Kweon, “UDH: Universal deep hiding for steganography, watermarking, and light field messaging,” presented at 34th Adv. Neural Inf. Process. Syst., Dec. 6–12, 2020.
21. Composition aware image steganography through adversarial self-generated supervision10.1109/TNNLS.2022.3175627 ↗
22. A. C. Gilbert, Y. Zhang, K. Lee, Y. Zhang, and H. Lee, “Towards understanding the invertibility of convolutional neural networks,” presented at 26th Int. Joint Conf. Artif. Intell., Melbourne, Australia, Aug. 19–25, 2017.10.24963/ijcai.2017/236 ↗
23. T. F. van der Ouderaa and D. E. Worrall, “Reversible GANs for memory efficient image-to-image translation,” presented at 2019 IEEE Conf. Comput. Vis. Pattern Recognit., Long Beach, CA, USA, Jun. 15–20, 2019.10.1109/CVPR.2019.00485 ↗
24. Y. Xu, C. Mou, Y. Hu, J. Xie, and J. Zhang, “Robust invertible image steganography,” presented at 2022 IEEE Conf. Comput. Vis. Pattern Recognit., New Orleans, LA, USA, Jun. 18–24, 2022.10.1109/CVPR52688.2022.00772 ↗
25. S. -P. Lu, R. Wang, T. Zhong, and P. L. Rosin, “Large-capacity image steganography based on invertible neural networks,” presented at 2021 IEEE Conf. Comput. Vis. Pattern Recognit., Nashville, TN, USA, Jun. 20–25, 2021.
26. J. Jing, X. Deng, M. Xu, J. Wang, and Z. Guan, “HiNet: Deep image hiding by invertible network,” presented at 2021 IEEE Int. Conf. Comput. Vis., Montreal, QC, Canada, Oct. 10–17, 2021.10.1109/ICCV48922.2021.00469 ↗
27. H. Fang, Y. Qiu, K. Chen, J. Zhang, W. Zhang, and E. -C. Chang, “Flow-based robust watermarking with invertible noise layer for blackbox distortions,” presented at 37th AAAI Conf. Artif. Intell., Washington, DC, USA, Feb. 7–14, 2023.10.1609/aaai.v37i4.25633 ↗
28. iSCMIS: Spatial-channel attention based deep invertible network for multi-image steganography10.1109/TMM.2023.3307970 ↗
29. R. G. Van Schyndel, A. Z. Tirkel, and C. F. Osborne, “A digital watermark,” presented at 1st Int. Conf. Img. Pro., Austin, TX, USA, Nov. 13–16, 1994.
30. N. Nikolaidis and I. Pitas, “Digital image watermarking: An overview,” presented at 6th IEEE Int. Conf. Multimed. Comput. Syst., Florence, Italy, Jun. 7–11, 1999.
31. A text image watermarking algorithm based on image enhancement
32. Visual-psnr measure of image quality10.1016/j.jvcir.2014.01.008 ↗
33. PSNR vs SSIM: Imperceptibility quality assessment for image steganography10.1007/s11042-020-10035-z ↗
34. R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” presented at 2018 IEEE Conf. Comput. Vis. Pattern Recognit., Salt Lake City, UT, USA, Jun. 18–22, 2018.10.1109/CVPR.2018.00068 ↗
35. J. Liu and X. He, “A review study on digital watermarking,” presented at 2005 Int. Conf. on Intell. Comput. Techn. Autom., Karachi, Pakistan, Aug. 27–28, 2005.
36. Fragility and robustness of binary-phaseonly-filter-based fragile/semifragile digital image watermarking10.1109/TIM.2007.911585 ↗
37. S. Tyagi, H. V. Singh, R. Agarwal, and S. K. Gangwar, “Digital watermarking techniques for security applications,” presented at 2016 Int. Conf. Emerg. Trends Elect. Elec. Sustainable Eng. Syst., Sultanpur, India, Mar. 11–12, 2016.10.1109/ICETEESES.2016.7581413 ↗
38. Novel blind multiple watermarking technique for images10.1109/TCSVT.2003.815948 ↗
39. A survey of digital watermarking techniques, applications and attacks
40. A high capacity watermarking technique for the printed document10.3390/electronics8121403 ↗
41. Security of fragile authentication watermarks with localization
42. Robust image watermarking theories and techniques: A review10.1016/S1665-6423(14)71612-8 ↗
43. M. S. Kankanhalli, Rajmohan, and K. Ramakrishnan, “Adaptive visible watermarking of images,” presented at 6th IEEE Int. Conf. Multimedia Comput. Syst., Florence, Italy, Jun. 7–11, 1999.
44. M. M. Yeung and F. Mintzer, “An invisible watermarking technique for image verification,” presented at 1997 Int. Conf. Img. Pro., Santa Barbara, CA, USA, Oct. 26–29, 1997.
45. F. M. Boland, J. J. O’Ruanaidh, and C. Dautzenberg, “Watermarking digital images for copyright protection,” presented at 15th Int. Conf. on Img. Pro. App., Edinburgh, Scotland, Jul. 4–6, 1995.10.1049/ip-vis:19960711 ↗
46. Information hiding using least significant bit steganography and cryptography10.5815/ijmecs.2012.06.04 ↗
47. Joint watermarking-encryption-ecc for patient record security in wavelet domain10.1109/MMUL.2020.2985973 ↗
48. D. R. Huang, J. F. Liu, J. W. Huang, and H. M. Liu, “A DWT-based image watermarking algorithm,” presented at IEEE Int. Conf. Multimed. Expo, Tokyo, Japan, Aug. 22–25, 2001.
49. Perceptual dft watermarking with improved detection and robustness to geometrical distortions10.1109/TIFS.2014.2322497 ↗
50. SVD-based digital image watermarking scheme10.1016/j.patrec.2005.01.004 ↗
51. Constructing immunized stego-image for secure steganography via artificial immune system10.1109/TMM.2023.3234812 ↗
52. S. Baluja, “Hiding images in plain sight: Deep steganography,” presented at 31st Adv. Neural Inf. Process. Syst., Long Beach, CA, USA, Dec. 4–9, 2017.
53. Hiding images within images10.1109/TPAMI.2019.2901877 ↗
54. T. Bui, S. Agarwal, N. Yu, and J. Collomosse, “RoSteALS: Robust steganography using autoencoder latent space,” presented at 2023 IEEE Conf. Comput. Vis. Pattern Recognit., Vancouver, BC, Canada, Jun. 17–24, 2023.10.1109/CVPRW59228.2023.00100 ↗
55. E. Wengrowski and K. Dana, “Light field messaging with deep photographic steganography,” presented at 2019 IEEE Conf. Comput. Vis. Pattern Recognit., Long Beach, CA, USA, Jun. 15–20, 2019.10.1109/CVPR.2019.00161 ↗
56. Deep template-based watermarking10.1109/TCSVT.2020.3009349 ↗
57. M. Tancik, B. Mildenhall, and R. Ng, “StegaStamp: Invisible hyperlinks in physical photographs,” presented at 2020 IEEE Conf. Comput. Vis. Pattern Recognit., Seattle, WA, USA, Jun. 14–19, 2020.10.1109/CVPR42600.2020.00219 ↗
58. Tera: Screen-to-camera image code with transparency, efficiency, robustness and adaptability10.1109/TMM.2021.3061801 ↗
59. J. Jia, Z. Gao, D. Zhu, X. Min, G. Zhai, and X. Yang, “Learning invisible markers for hidden codes in offline-to-online photography,” presented at 2022 IEEE Conf. Comput. Vis. Pattern Recognit., New Orleans, LA, USA, Jun. 18–24, 2022.10.1109/CVPR52688.2022.00231 ↗
60. H. Fang, Z. Jia, Z. Ma, E. -C. Chang, and W. Zhang, “PIMoG: An effective screen-shooting noise-layer simulation for deep-learning-based watermarking network,” presented at 30th ACM Int. Conf. on Multimed., Lisboa, Portugal, Oct. 10–14, 2022.10.1145/3503161.3548049 ↗
61. T. Teshima, I. Ishikawa, K. Tojo, K. Oono, M. Ikeda and M. Sugiyama, “Coupling-based invertible neural networks are universal diffeomorphism approximators,” presented at 34th Adv. Neural Inf. Process. Syst., Dec. 6–12, 2020.
62. Solving inverse problems using conditional invertible neural networks
63. Y. Lan, F. Shang, J. Yang, X. Kang, and E. Li, “Robust image steganography: Hiding messages in frequency coefficients,” presented at 37th AAAI Conf. Artif. Intell., Washington, DC, USA, Feb. 7–14, 2023.10.1609/aaai.v37i12.26746 ↗