Abstract
High-quality diverse image datasets, particularly those featuring faces and medical records, are essential for deep learning applications in computer vision. Protecting the copyright and privacy of such datasets remains a challenge, as existing blind watermarking methods fall short of effectively monitoring and restricting unauthorized access. To address this issue, we propose ImageShield, a responsibility-to-person blind watermarking mechanism for image datasets protection. It introduces a hybrid approach, combining traditional transform domain watermarking with an enhanced generative adversarial network (GAN), achieving an optimal balance between watermark imperceptibility and robustness. In the extraction phase, an optimized network architecture integrates the enhanced GAN with a specially designed attack layer, improving the efficiency of watermark feature retrieval, and reducing computational overhead. The method ensures high fidelity in extracting watermark features, even under potential distortions or attacks, by leveraging the robust structure of the GAN and attack layer. Experimental results on the Helen datasets and custom datasets demonstrate ImageShield’s superiority in terms of imperceptibility (PSNR: 37.2963 dB, SSIM: 0.9906), robustness, watermark embedding capacity, and execution efficiency. These contributions offer a novel solution to enhance the security and traceability of protected image datasets.
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Data available on request from the authors. The data that support the findings of this study are available from the corresponding author, upon reasonable request.
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Acknowledgements
All the authors are deeply grateful to the editors for smooth and fast handling of the manuscript. The authors would also like to thank the anonymous referees for their valuable suggestions to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Grant Nos. 61802111, 61872125), the Science and Technology Project of Henan Province (Grant Nos. 232102210109, 232102210096), Natural Science Foundation Project of Henan Province (Grant No. 242300421404), Key Scientific Research Projects of Colleges and Universities of Henan Province (Grant No. 24A520003), Pre-research Project of SongShan Laboratory (Grant No. YYJC012022011) and the Graduate Talent Program of Henan University (Grant Nos. SYLYC2022193 and SYLAL2023020).
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Zongwei Tang: Data curation; Formal analysis; Investigation; Methodology; Writing-original draft. Junyang Yu: Resources; Supervision; Investigation. Xiuli Chai: Conceptualization; Project administration; Resources; Supervision; Validation; Writing-reviewing and Editing. Tianfeng Ma: Software; Validation; Methodology. Zhihua Gan: Data curation; Investigation; Visualization. Binjie Wang: Project administration; Resources; Investigation.
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Tang, Z., Yu, J., Chai, X. et al. ImageShield: a responsibility-to-person blind watermarking mechanism for image datasets protection. Appl Intell 55, 84 (2025). https://doi.org/10.1007/s10489-024-06093-7
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DOI: https://doi.org/10.1007/s10489-024-06093-7