SIR-DCGAN: An Attention-Guided Robust Watermarking Method for Remote Sensing Image Protection Using Deep Convolutional Generative Adversarial Networks DOI Open Access

Shaoliang Pan,

Xiaojun Yin,

Mingrui Ding

и другие.

Electronics, Год журнала: 2025, Номер 14(9), С. 1853 - 1853

Опубликована: Май 1, 2025

Ensuring the security of remote sensing images is essential to prevent unauthorized access, tampering, and misuse. Deep learning-based digital watermarking offers a promising solution by embedding imperceptible information protect data integrity. This paper proposes SIR-DCGAN, an attention-guided robust method for image protection. It incorporates IR-FFM feature fusion module enhance reuse across different layers SE-AM attention mechanism emphasize critical watermark features. Additionally, noise simulation sub-network introduced improve resistance against common combined attacks. The proposed achieves high imperceptibility robustness while maintaining low computational cost. Extensive experiments on both natural datasets validate its effectiveness, with performance consistently surpassing existing approaches. These results demonstrate practicality reliability SIR-DCGAN secure distribution copyright

Язык: Английский

An adaptive ACM watermarking technique based on combined feature extraction and non-linear equation DOI
Abdelkader Laouid, Mostefa Kara, Brahim Ferik

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126954 - 126954

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
SIR-DCGAN: An Attention-Guided Robust Watermarking Method for Remote Sensing Image Protection Using Deep Convolutional Generative Adversarial Networks DOI Open Access

Shaoliang Pan,

Xiaojun Yin,

Mingrui Ding

и другие.

Electronics, Год журнала: 2025, Номер 14(9), С. 1853 - 1853

Опубликована: Май 1, 2025

Ensuring the security of remote sensing images is essential to prevent unauthorized access, tampering, and misuse. Deep learning-based digital watermarking offers a promising solution by embedding imperceptible information protect data integrity. This paper proposes SIR-DCGAN, an attention-guided robust method for image protection. It incorporates IR-FFM feature fusion module enhance reuse across different layers SE-AM attention mechanism emphasize critical watermark features. Additionally, noise simulation sub-network introduced improve resistance against common combined attacks. The proposed achieves high imperceptibility robustness while maintaining low computational cost. Extensive experiments on both natural datasets validate its effectiveness, with performance consistently surpassing existing approaches. These results demonstrate practicality reliability SIR-DCGAN secure distribution copyright

Язык: Английский

Процитировано

0